System

Abstract

An object of a system according to an embodiment is to naturally change a face of a person appearing in a video.SOLUTION: A system according to an embodiment includes an imaging unit, a detection unit, a change unit, and a provision unit. The photographing unit photographs an image. The detection unit detects a face of a person from an image captured by the imaging unit. The changing unit changes the face detected by the detecting unit to another face. The providing unit provides a video including the face changed by the changing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology protects privacy by blurring the faces of people captured in video footage, but this can create an unnatural appearance in the footage.

[0005] The system according to the embodiment aims to naturally change the face of a person appearing in a video. [Means for solving the problem]

[0006] The system according to the embodiment includes a capturing unit, a detecting unit, a changing unit, and a providing unit. The capturing unit captures video. The detecting unit detects a person's face from the video captured by the capturing unit. The changing unit changes the face detected by the detecting unit to another face. The providing unit provides video including the face changed by the changing unit. [Effects of the Invention]

[0007] The system according to the embodiment can naturally change the face of a person appearing in a video. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention changes the faces of people captured in the surrounding area in real time when live streaming a street scene. This system captures video, uses image generation AI to detect the faces of people in the video, and then changes the detected faces to other faces to ensure a natural look in the video. This allows live streaming while protecting the privacy of surrounding people. This allows the system to change the faces of people captured in the surrounding area in real time when live streaming a street scene. For example, when live streaming a tourist spot, even if the faces of surrounding tourists are captured, their faces are changed in real time, protecting their privacy. Similarly, when live streaming an event venue, the faces of surrounding people are changed, protecting their privacy. Furthermore, this technology prevents the video from appearing strange, providing a natural-looking image to viewers. For example, while pixelation can sometimes create a strange look in the video, this technology changes faces in a natural way, providing a natural-looking image to viewers.

[0029] A live streaming system according to an embodiment includes a camera unit, a detection unit, a modification unit, and a provision unit. The camera unit captures video footage during live streaming in the city. For example, when live streaming at a tourist spot or an event venue, the camera unit can capture a large number of people. The detection unit uses AI to detect human faces from the video footage captured by the camera unit. For example, facial recognition technology can be used to identify human faces in the video footage. The detection unit can detect faces using deep learning, for example. The detection unit can also detect faces using pattern recognition technology. The modification unit uses generation AI to change the face detected by the detection unit to another face. For example, the modification unit can generate a new face with the same facial expression and orientation as the original face. For example, if a smiling face is detected, the modification unit can generate a new face that also has a smiling face. The modification unit can also use generation AI to generate a new face based on extracted feature points. For example, the modification unit generates a new face based on feature points such as the positions of the eyes, nose, and mouth. The provision unit provides video footage including the face modified by the modification unit in real time. For example, the video can be provided as live streaming video. The providing unit can provide the video in real time within an allowable delay time range, for example. As a result, the live distribution system according to the embodiment can provide natural-looking video while protecting privacy by changing the faces of people in the video in real time.

[0030] The detection unit can identify the faces of people in the video using facial recognition technology. Facial recognition technology includes, for example, deep learning and pattern recognition technology. The detection unit can detect faces using, for example, deep learning. Deep learning learns large amounts of facial image data to achieve highly accurate facial recognition. The detection unit can also detect faces using, for example, pattern recognition technology. Pattern recognition technology identifies faces based on facial feature points. As a result, facial recognition technology can be used to accurately identify the faces of people in the video.

[0031] The modification unit can generate another face so that it has the same expression or orientation as the original face. Expressions include, for example, smiling, anger, surprise, etc. For example, if a smiling person's face is detected, the modification unit can generate a modified face so that it also has a smiling face. Orientations include, for example, facing forward, facing sideways, and facing diagonally. For example, if a face facing forward is detected, the modification unit can generate a modified face so that it also faces forward. In this way, by generating another face having the same expression and orientation as the original face, it is possible to prevent an unnatural appearance from appearing in the video.

[0032] The providing unit can provide the video including the modified face in real time. Real time includes, for example, providing the video within an acceptable delay time range. The providing unit can provide, for example, the video as live streaming video. For example, the providing unit can consider the video to be in real time when the delay time is within a few seconds. In this way, by providing the video including the modified face in real time, it is possible to provide a natural video to the viewer.

[0033] The detection unit can extract facial feature points of a person in the video. The feature points include, for example, the positions of the eyes, nose, and mouth. The detection unit can, for example, identify the position of the eyes and extract the feature points. The detection unit can, for example, identify the position of the nose and extract the feature points. The detection unit can, for example, identify the position of the mouth and extract the feature points. In this way, by extracting facial feature points, the accuracy of face modification is improved.

[0034] The modification unit can generate a new face based on the extracted feature points. Methods for generating a new face include, for example, generating a new face from an existing face database. The modification unit can generate a new face based on feature points such as the positions of the eyes, nose, and mouth. The modification unit can select a face that matches the feature points from an existing face database and generate it as a new face. This makes it possible to modify a face in a natural way by generating a new face based on feature points.

[0035] The imaging unit can analyze the surrounding environmental sounds when capturing images and automatically adjust appropriate imaging settings. For example, if the surroundings are noisy, the imaging unit can reduce noise by lowering the microphone sensitivity. For example, if the surroundings are quiet, the imaging unit can increase the microphone sensitivity to record clear audio. For example, if the surrounding sounds fluctuate, the imaging unit can adjust the sensitivity in real time. This allows optimal audio to be recorded by automatically adjusting the imaging settings according to the surrounding environmental sounds.

[0036] The imaging unit can detect the intensity and direction of light when capturing an image and automatically adjust the appropriate exposure settings. For example, if the light is too strong, the imaging unit can lower the exposure to prevent the image from being blown out. For example, if the light is weak, the imaging unit can raise the exposure to prevent the image from being too dark. For example, if the direction of the light changes, the imaging unit can adjust the exposure in real time. This allows optimal images to be captured by automatically adjusting the exposure settings according to the intensity and direction of the light.

[0037] The imaging unit can automatically adjust the position and angle of the camera when capturing an image to achieve appropriate framing. For example, if the subject moves, the imaging unit can automatically adjust the angle of the camera to fit the subject within the frame. For example, if the subject moves away, the imaging unit can zoom in to achieve optimal framing. For example, if the subject moves closer, the imaging unit can zoom out to achieve optimal framing. This makes it possible to achieve optimal framing by automatically adjusting the position and angle of the camera.

[0038] The imaging unit can detect the ambient temperature and humidity when taking a picture and optimize the operation of the camera. For example, when the temperature is high, the imaging unit can strengthen the camera's cooling function to stabilize operation. For example, when the temperature is low, the imaging unit can adjust the camera's operating speed to reduce battery consumption. For example, when the humidity is high, the imaging unit can activate a heater to prevent the lens from fogging up. This allows stable imaging by optimizing the camera's operation according to the ambient temperature and humidity.

[0039] When taking a picture, the photographing unit can predict the movements of people in the surrounding area and select the optimal timing for taking a picture. For example, the photographing unit can predict when people will gather and capture that moment. For example, the photographing unit can predict the direction people will move and capture the picture from the optimal position. For example, the photographing unit can predict when people will take a specific action and capture that moment. In this way, by predicting the movements of people in the surrounding area, the optimal timing for taking a picture can be selected.

[0040] The photographing unit can analyze the user's past photographing history and suggest appropriate photographing settings when photographing. For example, the photographing unit can automatically apply photographing settings that the user previously preferred. For example, the photographing unit can suggest optimal exposure and white balance based on the user's past photographing history. For example, the photographing unit can suggest optimal framing based on the user's past photographing history. In this way, optimal photographing settings can be suggested by analyzing the user's past photographing history.

[0041] The detection unit can improve detection accuracy by taking background information in the video into account when detecting a face. For example, when the background is complex, the detection unit can strengthen the face detection algorithm to improve accuracy. For example, when the background is simple, the detection unit can return the face detection algorithm to normal. For example, when the background is changing, the detection unit can update the background information in real time to maintain detection accuracy. In this way, by taking background information into account, the accuracy of face detection is improved.

[0042] During face detection, the detection unit can track the movement of a person in the video and continuously detect faces. For example, if a person is moving, the detection unit can track the movement and continuously detect faces. For example, if a person is standing still, the detection unit can temporarily stop face detection. For example, if the person starts moving again, the detection unit can resume face detection. This makes it possible to continuously detect faces by tracking the movement of a person.

[0043] The detection unit can improve the detection accuracy by correcting changes in light within the video during face detection. For example, if the light is too strong, the detection unit can improve the face detection accuracy by adjusting the exposure. For example, if the light is weak, the detection unit can improve the face detection accuracy by adjusting the exposure. For example, if the light changes drastically, the detection unit can maintain the face detection accuracy by adjusting the exposure in real time. In this way, the accuracy of face detection is improved by correcting changes in light.

[0044] The detection unit can analyze audio information in the video during face detection to improve face detection accuracy. For example, when the audio is loud, the detection unit can identify the position of the face based on the audio information to improve detection accuracy. For example, when the audio is quiet, the detection unit can perform face detection while ignoring the audio information. For example, when the audio fluctuates, the detection unit can analyze the audio information in real time to maintain detection accuracy. In this way, the accuracy of face detection is improved by analyzing the audio information.

[0045] The detection unit can improve the detection accuracy when detecting faces by taking into account the clothing and accessories of people in the video. For example, if a person is wearing a hat, the detection unit can perform face detection by taking into account the shape of the hat. For example, if a person is wearing glasses, the detection unit can perform face detection by taking into account the shape of the glasses. For example, if a person is wearing a mask, the detection unit can perform face detection by taking into account the shape of the mask. In this way, by taking into account clothing and accessories, the accuracy of face detection is improved.

[0046] The detection unit can improve the detection accuracy by estimating the age and gender of a person in the video when detecting a face. For example, in the case of an older person, the detection unit can improve the detection accuracy by taking into account facial features. For example, in the case of a younger person, the detection unit can improve the detection accuracy by taking into account facial features. For example, in the case of different genders, the detection unit can improve the detection accuracy by taking into account facial features. In this way, the accuracy of face detection is improved by estimating the age and gender.

[0047] When modifying a face, the modification unit can generate a natural-looking face by taking into account the direction and intensity of light in the video. For example, when the light is strong, the modification unit can generate a natural-looking face by taking into account shadows. For example, when the light is weak, the modification unit can generate a natural-looking face by adjusting the brightness. For example, when the direction of light changes, the modification unit can generate a natural-looking face by taking into account the direction of light in real time. This makes it possible to generate a natural-looking face by taking into account the direction and intensity of light.

[0048] When modifying a face, the modification unit can generate a natural-looking face by taking into consideration the skin color and texture of the person in the video. For example, if the skin color is light, the modification unit can generate a natural-looking face by taking into consideration the light skin color. For example, if the skin color is dark, the modification unit can generate a natural-looking face by taking into consideration the dark skin color. For example, if the skin texture is different, the modification unit can generate a natural-looking face by taking into consideration the texture. This makes it possible to generate a natural-looking face by taking into consideration the skin color and texture.

[0049] When changing the face, the change unit can generate a natural-looking face by taking into consideration the hairstyle and hair color of the person in the video. For example, if the hairstyle is short, the change unit can generate a natural-looking face by taking into consideration the short hairstyle. For example, if the hairstyle is long, the change unit can generate a natural-looking face by taking into consideration the long hairstyle. For example, if the hair color is different, the change unit can generate a natural-looking face by taking into consideration the hair color. This makes it possible to generate a natural-looking face by taking into consideration the hairstyle and hair color.

[0050] When modifying a face, the modification unit can generate a natural-looking face by taking into consideration the clothing and accessories of a person in the video. For example, if a person is wearing a hat, the modification unit can generate a natural-looking face by taking into consideration the shape of the hat. For example, if a person is wearing glasses, the modification unit can generate a natural-looking face by taking into consideration the shape of the glasses. For example, if a person is wearing a mask, the modification unit can generate a natural-looking face by taking into consideration the shape of the mask. This makes it possible to generate a natural-looking face by taking into consideration the clothing and accessories.

[0051] When changing the face, the modification unit can generate a natural-looking face by taking into consideration the age and gender of the person in the video. For example, in the case of an older person, the modification unit can generate a face with features according to the age. For example, in the case of a younger person, the modification unit can generate a face with features according to the age. For example, in the case of different genders, the modification unit can generate a face with features according to the gender. This makes it possible to generate a natural-looking face by taking into consideration the age and gender.

[0052] When changing the face, the modification unit can generate a natural-looking face by taking into account the movement of a person in the video. For example, if the person is moving, the modification unit can generate a natural-looking face that corresponds to the movement. For example, if the person is standing still, the modification unit can generate a natural-looking face that corresponds to the stationary state. For example, if the person starts moving again, the modification unit can generate a natural-looking face that corresponds to the movement. This makes it possible to generate a natural-looking face by taking into account the movement of the person.

[0053] When providing video, the providing unit can analyze the viewer's past viewing history and select an appropriate providing method. The providing unit can select the optimal providing method, for example, based on the video style that the viewer has preferred in the past. The providing unit can, for example, suggest videos that the viewer is likely to be interested in based on the viewer's past viewing history. The providing unit can, for example, analyze the viewer's past viewing history and provide the video style that the viewer has watched for the longest time. In this way, the optimal video providing method can be selected by analyzing the viewer's past viewing history.

[0054] When providing video, the providing unit can select an appropriate video providing method by taking into consideration the viewer's device information. For example, if the viewer is using a smartphone, the providing unit can provide video that matches the screen size. For example, if the viewer is using a tablet, the providing unit can provide video that is optimized for a large screen. For example, if the viewer is using a smartwatch, the providing unit can provide video that is concise and highly visible. This makes it possible to select the optimal video providing method by taking into consideration the viewer's device information.

[0055] The providing unit can select an appropriate video providing method in consideration of the viewer's network environment when providing video. For example, if the network speed is slow, the providing unit can provide low-resolution video. For example, if the network speed is fast, the providing unit can provide high-resolution video. For example, if the network speed fluctuates, the providing unit can adjust the resolution in real time to provide video. This allows the optimal video providing method to be selected in consideration of the viewer's network environment.

[0056] When providing video, the providing unit can select an appropriate video providing method by taking into consideration the viewer's geographical location information. For example, if the viewer is in a specific area, the providing unit can provide video related to that area. For example, if the viewer is traveling, the providing unit can provide video related to the viewer's travel destination. For example, if the viewer is at home, the providing unit can provide video that can be enjoyed at home. In this way, the optimal video providing method can be selected by taking into consideration the viewer's geographical location information.

[0057] The providing unit can analyze the viewer's social media activity when providing the video and provide related video. For example, the providing unit can provide video related to a location where the viewer has checked in on social media. For example, the providing unit can analyze the content posted by the viewer on social media and provide related video. For example, the providing unit can provide related video by referring to the activity of the viewer's friends on social media. In this way, related video can be provided by analyzing the viewer's social media activity.

[0058] The providing unit can customize the providing method by reflecting viewer feedback when providing video. The providing unit can customize the next providing method, for example, based on the viewer's preferred video style. The providing unit can adjust the content of the video, for example, based on viewer feedback. The providing unit can adjust the providing order of the videos, for example, based on viewer feedback. In this way, the providing method can be customized by reflecting viewer feedback.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When shooting, the camera unit can analyze the surrounding environmental sounds and automatically adjust the appropriate shooting settings. For example, if the surroundings are noisy, the microphone sensitivity can be lowered to reduce noise. If the surroundings are quiet, the microphone sensitivity can be increased to record clear audio. If the surrounding sounds fluctuate, the sensitivity can be adjusted in real time. This allows the camera to automatically adjust the shooting settings according to the surrounding environmental sounds and record optimal audio.

[0061] The shooting unit detects the intensity and direction of light when shooting and can automatically adjust the appropriate exposure settings. For example, if the light is too strong, the exposure can be lowered to prevent the image from being blown out. If the light is weak, the exposure can be raised to prevent the image from being too dark. If the direction of the light changes, the exposure can be adjusted in real time. This allows the optimal image to be captured by automatically adjusting the exposure settings according to the intensity and direction of the light.

[0062] The shooting unit can automatically adjust the camera position and angle when shooting to ensure appropriate framing. For example, if the subject moves, the camera angle can be automatically adjusted to fit the subject within the frame. If the subject moves away, the camera can zoom in to ensure optimal framing. If the subject moves closer, the camera can zoom out to ensure optimal framing. This makes it possible to automatically adjust the camera position and angle to ensure optimal framing.

[0063] The camera unit detects the ambient temperature and humidity when shooting and can optimize the camera's operation. For example, when the temperature is high, the camera's cooling function can be strengthened to stabilize operation. When the temperature is low, the camera's operating speed can be adjusted to reduce battery consumption. When the humidity is high, a heater can be activated to prevent the lens from fogging up. This allows the camera's operation to be optimized according to the ambient temperature and humidity, enabling stable shooting.

[0064] The detection unit can improve detection accuracy by taking into account background information in the video when detecting faces. For example, if the background is complex, the face detection algorithm can be strengthened to improve accuracy. If the background is simple, the face detection algorithm can be returned to normal. If the background changes, the background information can be updated in real time to maintain detection accuracy. In this way, by taking background information into account, the accuracy of face detection is improved.

[0065] During face detection, the detection unit can track the movement of a person in the video and continuously detect faces. For example, if a person is moving, the movement can be tracked and faces can be continuously detected. If the person is standing still, face detection can be temporarily stopped. If the person starts moving again, face detection can be resumed. This makes it possible to continuously detect faces by tracking the movement of a person.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The camera crew shoots footage of the live broadcast in the city. For example, if the live broadcast is being done at a tourist spot or an event venue, the camera crew can capture many people. Step 2: The detection unit uses AI to detect faces from the video captured by the camera unit. For example, it can use facial recognition technology, deep learning, and pattern recognition technology to identify faces in the video. Step 3: The modification unit uses the generation AI to modify the face detected by the detection unit into a different face. For example, it can generate a different face with the same facial expression and pose as the original face. It can also generate a new face based on the extracted feature points. Step 4: The providing unit provides the video including the face modified by the modifying unit in real time. For example, the video may be provided as live streaming video. The providing unit may provide the video in real time within an allowable delay range.

[0068] (Example 2) A system according to an embodiment of the present invention changes the faces of people captured in the surrounding area in real time when live streaming a street scene. This system captures video, uses image generation AI to detect the faces of people in the video, and then changes the detected faces to other faces to ensure a natural look in the video. This allows live streaming while protecting the privacy of surrounding people. This allows the system to change the faces of people captured in the surrounding area in real time when live streaming a street scene. For example, when live streaming a tourist spot, even if the faces of surrounding tourists are captured, their faces are changed in real time, protecting their privacy. Similarly, when live streaming an event venue, the faces of surrounding people are changed, protecting their privacy. Furthermore, this technology prevents the video from appearing strange, providing a natural-looking image to viewers. For example, while pixelation can sometimes create a strange look in the video, this technology changes faces in a natural way, providing a natural-looking image to viewers.

[0069] A live streaming system according to an embodiment includes a camera unit, a detection unit, a modification unit, and a provision unit. The camera unit captures video footage during live streaming in the city. For example, when live streaming at a tourist spot or an event venue, the camera unit can capture a large number of people. The detection unit uses AI to detect human faces from the video footage captured by the camera unit. For example, facial recognition technology can be used to identify human faces in the video footage. The detection unit can detect faces using deep learning, for example. The detection unit can also detect faces using pattern recognition technology. The modification unit uses generation AI to change the face detected by the detection unit to another face. For example, the modification unit can generate a new face with the same facial expression and orientation as the original face. For example, if a smiling face is detected, the modification unit can generate a new face that also has a smiling face. The modification unit can also use generation AI to generate a new face based on extracted feature points. For example, the modification unit generates a new face based on feature points such as the positions of the eyes, nose, and mouth. The provision unit provides video footage including the face modified by the modification unit in real time. For example, the video can be provided as live streaming video. The providing unit can provide the video in real time within an allowable delay time range, for example. As a result, the live distribution system according to the embodiment can provide natural-looking video while protecting privacy by changing the faces of people in the video in real time.

[0070] The detection unit can identify the faces of people in the video using facial recognition technology. Facial recognition technology includes, for example, deep learning and pattern recognition technology. The detection unit can detect faces using, for example, deep learning. Deep learning learns large amounts of facial image data to achieve highly accurate facial recognition. The detection unit can also detect faces using, for example, pattern recognition technology. Pattern recognition technology identifies faces based on facial feature points. As a result, facial recognition technology can be used to accurately identify the faces of people in the video.

[0071] The modification unit can generate another face so that it has the same expression or orientation as the original face. Expressions include, for example, smiling, anger, surprise, etc. For example, if a smiling person's face is detected, the modification unit can generate a modified face so that it also has a smiling face. Orientations include, for example, facing forward, facing sideways, and facing diagonally. For example, if a face facing forward is detected, the modification unit can generate a modified face so that it also faces forward. In this way, by generating another face having the same expression and orientation as the original face, it is possible to prevent an unnatural appearance from appearing in the video.

[0072] The providing unit can provide the video including the modified face in real time. Real time includes, for example, providing the video within an acceptable delay time range. The providing unit can provide, for example, the video as live streaming video. For example, the providing unit can consider the video to be in real time when the delay time is within a few seconds. In this way, by providing the video including the modified face in real time, it is possible to provide a natural video to the viewer.

[0073] The detection unit can extract facial feature points of a person in the video. The feature points include, for example, the positions of the eyes, nose, and mouth. The detection unit can, for example, identify the position of the eyes and extract the feature points. The detection unit can, for example, identify the position of the nose and extract the feature points. The detection unit can, for example, identify the position of the mouth and extract the feature points. In this way, by extracting facial feature points, the accuracy of face modification is improved.

[0074] The modification unit can generate a new face based on the extracted feature points. Methods for generating a new face include, for example, generating a new face from an existing face database. The modification unit can generate a new face based on feature points such as the positions of the eyes, nose, and mouth. The modification unit can select a face that matches the feature points from an existing face database and generate it as a new face. This makes it possible to modify a face in a natural way by generating a new face based on feature points.

[0075] The camera unit can estimate the user's emotions and automatically adjust the timing of shooting based on the estimated user emotions. For example, if the user is nervous, the camera unit can delay shooting until the user relaxes. For example, if the user is excited, the camera unit can pause shooting until the user's excitement subsides. For example, if the user is relaxed, the camera unit can immediately start shooting. This allows for adjusting the timing of shooting according to the user's emotions, thereby enabling more appropriate footage to be captured. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0076] The imaging unit can analyze the surrounding environmental sounds when capturing images and automatically adjust appropriate imaging settings. For example, if the surroundings are noisy, the imaging unit can reduce noise by lowering the microphone sensitivity. For example, if the surroundings are quiet, the imaging unit can increase the microphone sensitivity to record clear audio. For example, if the surrounding sounds fluctuate, the imaging unit can adjust the sensitivity in real time. This allows optimal audio to be recorded by automatically adjusting the imaging settings according to the surrounding environmental sounds.

[0077] The imaging unit can detect the intensity and direction of light when capturing an image and automatically adjust the appropriate exposure settings. For example, if the light is too strong, the imaging unit can lower the exposure to prevent the image from being blown out. For example, if the light is weak, the imaging unit can raise the exposure to prevent the image from being too dark. For example, if the direction of the light changes, the imaging unit can adjust the exposure in real time. This allows optimal images to be captured by automatically adjusting the exposure settings according to the intensity and direction of the light.

[0078] The imaging unit can automatically adjust the position and angle of the camera when capturing an image to achieve appropriate framing. For example, if the subject moves, the imaging unit can automatically adjust the angle of the camera to fit the subject within the frame. For example, if the subject moves away, the imaging unit can zoom in to achieve optimal framing. For example, if the subject moves closer, the imaging unit can zoom out to achieve optimal framing. This makes it possible to achieve optimal framing by automatically adjusting the position and angle of the camera.

[0079] The image capture unit can estimate the user's emotions and automatically determine the priority of scenes to capture based on the estimated user emotions. For example, if the user is excited, the image capture unit can prioritize capturing scenes that cause excitement. For example, if the user is relaxed, the image capture unit can prioritize capturing scenes that help the user relax. For example, if the user is nervous, the image capture unit can prioritize capturing scenes that help the user relieve tension. This allows more appropriate scenes to be captured by determining the priority of scenes to capture according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] The imaging unit can detect the ambient temperature and humidity when taking a picture and optimize the operation of the camera. For example, when the temperature is high, the imaging unit can strengthen the camera's cooling function to stabilize operation. For example, when the temperature is low, the imaging unit can adjust the camera's operating speed to reduce battery consumption. For example, when the humidity is high, the imaging unit can activate a heater to prevent the lens from fogging up. This allows stable imaging by optimizing the camera's operation according to the ambient temperature and humidity.

[0081] When taking a picture, the photographing unit can predict the movements of people in the surrounding area and select the optimal timing for taking a picture. For example, the photographing unit can predict when people will gather and capture that moment. For example, the photographing unit can predict the direction people will move and capture the picture from the optimal position. For example, the photographing unit can predict when people will take a specific action and capture that moment. In this way, by predicting the movements of people in the surrounding area, the optimal timing for taking a picture can be selected.

[0082] The photographing unit can analyze the user's past photographing history and suggest appropriate photographing settings when photographing. For example, the photographing unit can automatically apply photographing settings that the user previously preferred. For example, the photographing unit can suggest optimal exposure and white balance based on the user's past photographing history. For example, the photographing unit can suggest optimal framing based on the user's past photographing history. In this way, optimal photographing settings can be suggested by analyzing the user's past photographing history.

[0083] The detection unit can estimate the user's emotion and automatically adjust the accuracy of face detection based on the estimated user's emotion. For example, if the user is nervous, the detection unit can increase the accuracy of face detection to ensure reliable detection. For example, if the user is relaxed, the detection unit can return the accuracy of face detection to normal. For example, if the user is excited, the detection unit can increase the accuracy of face detection to ensure rapid detection. This allows for more accurate face detection by adjusting the accuracy of face detection according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] The detection unit can improve detection accuracy by taking background information in the video into account when detecting a face. For example, when the background is complex, the detection unit can strengthen the face detection algorithm to improve accuracy. For example, when the background is simple, the detection unit can return the face detection algorithm to normal. For example, when the background is changing, the detection unit can update the background information in real time to maintain detection accuracy. In this way, by taking background information into account, the accuracy of face detection is improved.

[0085] During face detection, the detection unit can track the movement of a person in the video and continuously detect faces. For example, if a person is moving, the detection unit can track the movement and continuously detect faces. For example, if a person is standing still, the detection unit can temporarily stop face detection. For example, if the person starts moving again, the detection unit can resume face detection. This makes it possible to continuously detect faces by tracking the movement of a person.

[0086] The detection unit can improve the detection accuracy by correcting changes in light within the video during face detection. For example, if the light is too strong, the detection unit can improve the face detection accuracy by adjusting the exposure. For example, if the light is weak, the detection unit can improve the face detection accuracy by adjusting the exposure. For example, if the light changes drastically, the detection unit can maintain the face detection accuracy by adjusting the exposure in real time. In this way, the accuracy of face detection is improved by correcting changes in light.

[0087] The detection unit can estimate the user's emotions and automatically determine the priority of faces to be detected based on the estimated user's emotions. For example, if the user is excited, the detection unit can preferentially detect faces of people who cause excitement. For example, if the user is relaxed, the detection unit can preferentially detect faces of people who help the user relax. For example, if the user is nervous, the detection unit can preferentially detect faces of people who help the user relieve tension. This enables more appropriate face detection by determining the priority of faces according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] The detection unit can analyze audio information in the video during face detection to improve face detection accuracy. For example, when the audio is loud, the detection unit can identify the position of the face based on the audio information to improve detection accuracy. For example, when the audio is quiet, the detection unit can perform face detection while ignoring the audio information. For example, when the audio fluctuates, the detection unit can analyze the audio information in real time to maintain detection accuracy. In this way, the accuracy of face detection is improved by analyzing the audio information.

[0089] The detection unit can improve the detection accuracy when detecting faces by taking into account the clothing and accessories of people in the video. For example, if a person is wearing a hat, the detection unit can perform face detection by taking into account the shape of the hat. For example, if a person is wearing glasses, the detection unit can perform face detection by taking into account the shape of the glasses. For example, if a person is wearing a mask, the detection unit can perform face detection by taking into account the shape of the mask. In this way, by taking into account clothing and accessories, the accuracy of face detection is improved.

[0090] The detection unit can improve the detection accuracy by estimating the age and gender of a person in the video when detecting a face. For example, in the case of an older person, the detection unit can improve the detection accuracy by taking into account facial features. For example, in the case of a younger person, the detection unit can improve the detection accuracy by taking into account facial features. For example, in the case of different genders, the detection unit can improve the detection accuracy by taking into account facial features. In this way, the accuracy of face detection is improved by estimating the age and gender.

[0091] The modification unit can estimate the user's emotion and automatically adjust the facial expression to be changed based on the estimated user's emotion. For example, if the user is relaxed, the modification unit can generate a face with a relaxed expression. For example, if the user is excited, the modification unit can generate a face with an excited expression. For example, if the user is nervous, the modification unit can generate a face with a nervous expression. This allows for a more natural face change by adjusting the facial expression according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0092] When modifying a face, the modification unit can generate a natural-looking face by taking into account the direction and intensity of light in the video. For example, when the light is strong, the modification unit can generate a natural-looking face by taking into account shadows. For example, when the light is weak, the modification unit can generate a natural-looking face by adjusting the brightness. For example, when the direction of light changes, the modification unit can generate a natural-looking face by taking into account the direction of light in real time. This makes it possible to generate a natural-looking face by taking into account the direction and intensity of light.

[0093] When modifying a face, the modification unit can generate a natural-looking face by taking into consideration the skin color and texture of the person in the video. For example, if the skin color is light, the modification unit can generate a natural-looking face by taking into consideration the light skin color. For example, if the skin color is dark, the modification unit can generate a natural-looking face by taking into consideration the dark skin color. For example, if the skin texture is different, the modification unit can generate a natural-looking face by taking into consideration the texture. This makes it possible to generate a natural-looking face by taking into consideration the skin color and texture.

[0094] When changing the face, the change unit can generate a natural-looking face by taking into consideration the hairstyle and hair color of the person in the video. For example, if the hairstyle is short, the change unit can generate a natural-looking face by taking into consideration the short hairstyle. For example, if the hairstyle is long, the change unit can generate a natural-looking face by taking into consideration the long hairstyle. For example, if the hair color is different, the change unit can generate a natural-looking face by taking into consideration the hair color. This makes it possible to generate a natural-looking face by taking into consideration the hairstyle and hair color.

[0095] The modification unit can estimate the user's emotion and automatically adjust the facial features to be modified based on the estimated user's emotion. For example, if the user is relaxed, the modification unit can generate a face with relaxed features. For example, if the user is excited, the modification unit can generate a face with excited features. For example, if the user is nervous, the modification unit can generate a face with nervous features. This allows for a more natural face modification by adjusting the facial features according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0096] When modifying a face, the modification unit can generate a natural-looking face by taking into consideration the clothing and accessories of a person in the video. For example, if a person is wearing a hat, the modification unit can generate a natural-looking face by taking into consideration the shape of the hat. For example, if a person is wearing glasses, the modification unit can generate a natural-looking face by taking into consideration the shape of the glasses. For example, if a person is wearing a mask, the modification unit can generate a natural-looking face by taking into consideration the shape of the mask. This makes it possible to generate a natural-looking face by taking into consideration the clothing and accessories.

[0097] When changing the face, the modification unit can generate a natural-looking face by taking into consideration the age and gender of the person in the video. For example, in the case of an older person, the modification unit can generate a face with features according to the age. For example, in the case of a younger person, the modification unit can generate a face with features according to the age. For example, in the case of different genders, the modification unit can generate a face with features according to the gender. This makes it possible to generate a natural-looking face by taking into consideration the age and gender.

[0098] When changing the face, the modification unit can generate a natural-looking face by taking into account the movement of a person in the video. For example, if the person is moving, the modification unit can generate a natural-looking face that corresponds to the movement. For example, if the person is standing still, the modification unit can generate a natural-looking face that corresponds to the stationary state. For example, if the person starts moving again, the modification unit can generate a natural-looking face that corresponds to the movement. This makes it possible to generate a natural-looking face by taking into account the movement of the person.

[0099] The providing unit can estimate the user's emotions and automatically adjust the video presentation method based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide videos at a leisurely pace. For example, if the user is excited, the providing unit can provide videos with visually stimulating effects. For example, if the user is nervous, the providing unit can provide simple, highly visible videos. This allows for more appropriate videos to be provided by adjusting the video presentation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0100] When providing video, the providing unit can analyze the viewer's past viewing history and select an appropriate providing method. The providing unit can select the optimal providing method, for example, based on the video style that the viewer has preferred in the past. The providing unit can, for example, suggest videos that the viewer is likely to be interested in based on the viewer's past viewing history. The providing unit can, for example, analyze the viewer's past viewing history and provide the video style that the viewer has watched for the longest time. In this way, the optimal video providing method can be selected by analyzing the viewer's past viewing history.

[0101] When providing video, the providing unit can select an appropriate video providing method by taking into consideration the viewer's device information. For example, if the viewer is using a smartphone, the providing unit can provide video that matches the screen size. For example, if the viewer is using a tablet, the providing unit can provide video that is optimized for a large screen. For example, if the viewer is using a smartwatch, the providing unit can provide video that is concise and highly visible. This makes it possible to select the optimal video providing method by taking into consideration the viewer's device information.

[0102] The providing unit can select an appropriate video providing method in consideration of the viewer's network environment when providing video. For example, if the network speed is slow, the providing unit can provide low-resolution video. For example, if the network speed is fast, the providing unit can provide high-resolution video. For example, if the network speed fluctuates, the providing unit can adjust the resolution in real time to provide video. This allows the optimal video providing method to be selected in consideration of the viewer's network environment.

[0103] The providing unit can estimate the user's emotions and adjust the order in which the videos are provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can prioritize providing relaxing videos. For example, if the user is excited, the providing unit can prioritize providing exciting videos. For example, if the user is tense, the providing unit can prioritize providing videos that relieve tension. In this way, by adjusting the order in which the videos are provided according to the user's emotions, more appropriate videos can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0104] When providing video, the providing unit can select an appropriate video providing method by taking into consideration the viewer's geographical location information. For example, if the viewer is in a specific area, the providing unit can provide video related to that area. For example, if the viewer is traveling, the providing unit can provide video related to the viewer's travel destination. For example, if the viewer is at home, the providing unit can provide video that can be enjoyed at home. In this way, the optimal video providing method can be selected by taking into consideration the viewer's geographical location information.

[0105] The providing unit can analyze the viewer's social media activity when providing the video and provide related video. For example, the providing unit can provide video related to a location where the viewer has checked in on social media. For example, the providing unit can analyze the content posted by the viewer on social media and provide related video. For example, the providing unit can provide related video by referring to the activity of the viewer's friends on social media. In this way, related video can be provided by analyzing the viewer's social media activity.

[0106] The providing unit can customize the providing method by reflecting viewer feedback when providing video. The providing unit can customize the next providing method, for example, based on the viewer's preferred video style. The providing unit can adjust the content of the video, for example, based on viewer feedback. The providing unit can adjust the providing order of the videos, for example, based on viewer feedback. In this way, the providing method can be customized by reflecting viewer feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-described photographing unit, detection unit, modification unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the smart device 14 and captures video when live streaming on the street. The detection unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and detects a person's face from the captured video using AI. The modification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and changes the detected face to another face using generation AI. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides video including the modified face in real time. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described capturing unit, detecting unit, modifying unit, and providing unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the capturing unit is realized by the camera 42 of the smart glasses 214 and captures video when live streaming on the street. The detecting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects a person's face from the captured video using AI. The modifying unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and changes the detected face to another face using generation AI. The providing unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides video including the modified face in real time. === Hard Collateral 1-3 === Each of the multiple elements including the above-described photographing unit, detecting unit, changing unit, and providing unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the headset type terminal 314 and captures video when live streaming on the street. The detecting unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and detects a person's face from the captured video using AI. The changing unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and changes the detected face to another face using generation AI. The providing unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides video including the changed face in real time. === Hard Collateral 1-4 === Each of the multiple elements including the above-described photographing unit, detection unit, modification unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the robot 414 and captures video when live streaming in the city. The detection unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and detects a person's face from the captured video using AI. The modification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and changes the detected face to another face using generation AI. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides video including the modified face in real time.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The image capturing unit can estimate the user's emotions and automatically adjust the timing of image capture based on the estimated user emotions. For example, if the user is nervous, image capture can be delayed until the user relaxes. If the user is excited, image capture can be paused until the user calms down. If the user is relaxed, image capture can be started immediately. In this way, more appropriate video can be captured by adjusting the timing of image capture according to the user's emotions.

[0109] When shooting, the camera unit can analyze the surrounding environmental sounds and automatically adjust the appropriate shooting settings. For example, if the surroundings are noisy, the microphone sensitivity can be lowered to reduce noise. If the surroundings are quiet, the microphone sensitivity can be increased to record clear audio. If the surrounding sounds fluctuate, the sensitivity can be adjusted in real time. This allows the camera to automatically adjust the shooting settings according to the surrounding environmental sounds and record optimal audio.

[0110] The shooting unit detects the intensity and direction of light when shooting and can automatically adjust the appropriate exposure settings. For example, if the light is too strong, the exposure can be lowered to prevent the image from being blown out. If the light is weak, the exposure can be raised to prevent the image from being too dark. If the direction of the light changes, the exposure can be adjusted in real time. This allows the optimal image to be captured by automatically adjusting the exposure settings according to the intensity and direction of the light.

[0111] The shooting unit can automatically adjust the camera position and angle when shooting to ensure appropriate framing. For example, if the subject moves, the camera angle can be automatically adjusted to fit the subject within the frame. If the subject moves away, the camera can zoom in to ensure optimal framing. If the subject moves closer, the camera can zoom out to ensure optimal framing. This makes it possible to automatically adjust the camera position and angle to ensure optimal framing.

[0112] The camera unit detects the ambient temperature and humidity when shooting and can optimize the camera's operation. For example, when the temperature is high, the camera's cooling function can be strengthened to stabilize operation. When the temperature is low, the camera's operating speed can be adjusted to reduce battery consumption. When the humidity is high, a heater can be activated to prevent the lens from fogging up. This allows the camera's operation to be optimized according to the ambient temperature and humidity, enabling stable shooting.

[0113] The photographing unit can estimate the user's emotions and automatically determine the priority of scenes to be photographed based on the estimated user's emotions. For example, if the user is excited, scenes that cause excitement can be photographed preferentially. If the user is relaxed, scenes that help the user to relax can be photographed preferentially. If the user is tense, scenes that help the user to relieve tension can be photographed preferentially. In this way, by determining the priority of scenes to be photographed according to the user's emotions, more appropriate scenes can be photographed.

[0114] The detection unit can estimate the user's emotions and automatically adjust the accuracy of face detection based on the estimated user emotions. For example, if the user is nervous, the accuracy of face detection can be increased to ensure reliable detection. If the user is relaxed, the accuracy of face detection can be returned to normal. If the user is excited, the accuracy of face detection can be increased to ensure rapid detection. In this way, more accurate face detection is possible by adjusting the accuracy of face detection according to the user's emotions.

[0115] The detection unit can improve detection accuracy by taking into account background information in the video when detecting faces. For example, if the background is complex, the face detection algorithm can be strengthened to improve accuracy. If the background is simple, the face detection algorithm can be returned to normal. If the background changes, the background information can be updated in real time to maintain detection accuracy. In this way, by taking background information into account, the accuracy of face detection is improved.

[0116] During face detection, the detection unit can track the movement of a person in the video and continuously detect faces. For example, if a person is moving, the movement can be tracked and faces can be continuously detected. If the person is standing still, face detection can be temporarily stopped. If the person starts moving again, face detection can be resumed. This makes it possible to continuously detect faces by tracking the movement of a person.

[0117] The detection unit can estimate the user's emotions and automatically determine the priority of faces to be detected based on the estimated user's emotions. For example, if the user is excited, it can prioritize detecting faces of people who cause excitement. If the user is relaxed, it can prioritize detecting faces of people who help the user relax. If the user is tense, it can prioritize detecting faces of people who help the user relieve tension. This enables more appropriate face detection by determining the priority of faces according to the user's emotions.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The camera crew shoots footage of the live broadcast in the city. For example, if the live broadcast is being done at a tourist spot or an event venue, the camera crew can capture many people. Step 2: The detection unit uses AI to detect faces from the video captured by the camera unit. For example, it can use facial recognition technology, deep learning, and pattern recognition technology to identify faces in the video. Step 3: The modification unit uses the generation AI to modify the face detected by the detection unit into a different face. For example, it can generate a different face with the same facial expression and pose as the original face. It can also generate a new face based on the extracted feature points. Step 4: The providing unit provides the video including the face modified by the modifying unit in real time. For example, the video may be provided as live streaming video. The providing unit may provide the video in real time within an allowable delay range.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0183] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A filming unit that shoots the video; a detection unit that detects a person's face from the video captured by the imaging unit; a change unit that changes the face detected by the detection unit to another face; a providing unit that provides a video including the face changed by the changing unit; Equipped with A system characterized by:

2. The detection unit Identifying people's faces in video using facial recognition technology 2. The system of claim 1.

3. The change unit Generate another face with the same expression or pose as the original face 2. The system of claim 1.

4. The providing unit Providing real-time footage with altered faces 2. The system of claim 1.

5. The detection unit Extracting facial features from a person in a video 2. The system of claim 1.

6. The change unit Generate a new face based on the extracted feature points 2. The system of claim 1.

7. The imaging unit is Estimates the user's emotions and automatically adjusts the timing of taking photos based on the estimated user emotions.

2. The system of claim 1.

8. The imaging unit is When shooting, analyzes the surrounding environmental sounds and automatically adjusts the appropriate shooting settings.

2. The system of claim 1.

Images