195 results about "Facial identity" patented technology

The present invention provides a facial recognition system using deep learning methodologies while integrating privacy-preserving capabilities. This system employs convolutional neural networks (CNNs) to extract and classify facial features, ensuring high accuracy in recognition tasks. Moreover, the system addresses privacy concerns by incorporating techniques such as facial feature encryption and anonymization, thereby enhancing user privacy and data security. This invention is applicable across various domains, including security, surveillance, access control, and personalized services, where facial recognition is utilized while preserving individual privacy.

The invention discloses an emotion detection system based on facial recognition, and relates to the technical field of computer vision and emotion calculation. A video stream time sequence analysis module is used for extracting a facial micro-expression image sequence of continuous frames, a time sequence feature vector containing a micro-expression intensity gradient, an illumination robustness coefficient and a facial action unit cooperation feature is generated, and a multi-mode dynamic sensing module is combined to carry out real-time analysis on an emotion classification probability, voice emotion parameters and physiological signals. And the fusion decision module performs dynamic weighted fusion on the multi-modal data based on the scene adaptive weight, and finally generates a comprehensive emotion score. Through multi-modal time sequence modeling and a dynamic weight optimization mechanism, the accuracy and environmental adaptability of emotion recognition are remarkably improved, and real-time perception and accurate decision making of customer emotion are realized in a target scene.

The invention relates to the technical field of face detection, and particularly discloses a face recognition system based on a face mask detection model, which is used for solving the problems of face recognition accuracy reduction and counterfeiting risk caused by mask shielding, counterfeiting attack and multi-source environment interference in high-security sensitive scenes such as a banking business window and the like. The system comprises a data acquisition and enhancement module, a feature extraction and multi-task neural network discrimination module, a counterfeit attack detection and risk blocking module, and a system deployment and model adaptive training module. According to the invention, through integration of multi-channel high-resolution visible light and infrared imaging, forgery attack sample enhancement based on the generative adversarial network, adaptive difficult sample sampling and multi-task deep discrimination network collaborative optimization, efficient recognition and real-time risk blocking of real and forgery mask faces are realized; and the security and robustness of a face recognition system in scenes such as banks are improved.

Disclosed are a method and system to protect user facial privacy against unknown face recognition levels without compromising on a user's online experience. An input source to input an original face image. A training circuit configured to train a generator model to output an image that resembles the original face image. An optimizer configured to generate a protected face image based on the trained model that fools a black-box face recognition model, while imitating a makeup style. A display device to display the protected face image online.

One embodiment of the present invention sets forth a technique for performing face swapping. The technique includes converting a first input image that depicts a first facial identity from a first viewpoint at a first time into a first latent representation and converting a second input image that depicts the first facial identity from a second viewpoint at the first time into a second latent representation. The technique also includes generating, via a first machine learning model, a first output image that depicts a second facial identity from the first viewpoint based on the first latent representation. The technique further includes generating, via the first machine learning model, a second output image that depicts the second facial identity from the second viewpoint based on the second latent representation.

In some embodiments, a method generates a first representation of a first image including a first facial identity and generates an identity representation from a second image that describes a second facial identity of the second image. The identity representation is mapped to a set of low-rank adaptation weights. The method adapts the first representation to an adapted first representation using the set of low-rank adaptation weights that are applied to a layer in a model. Decoder input values are generated based on the adapted first representation. The method performs decoding using the decoder input values to generate an output image. The output image swaps the first facial identity of the first image with the second facial identity of the second image..

This overcomes the difficulties associated with introducing facial recognition to apartment complexes and maximizes its convenience. [Solution] This problem is solved by an authentication system that includes a face data storage unit that stores facial images of residents of an apartment complex or facial data that is processed data thereof, and an authentication unit that compares faces photographed by a photographing means with the facial data and unlocks the doors of each facility in the apartment complex, where the photographing means includes a first photographing means installed near the entrance door of the apartment complex building and a second photographing means installed near the front doors of multiple dwelling units in the apartment complex, and the front doors of each dwelling unit are automatically locked by an auto-lock function.

An augmented reality headset, with an eye tracking device, and a secondary user input device, such as, a brain wave sensor detecting the user's brain waves, a microphone detecting user sounds, or finger recognition sensor, or facial recognition sensor detecting facial gestures. When entering an elevator, the user views elevator input icons, displayed in mid-air. The user gazes at, an elevator floor icon, a cursor follows the gaze to the icon. The user thinks click, or says enter, or finger gestures, or facial gestures, which activates the icon. The activated floor icon, directs the elevator, to move to the floor. The input icons can be associated with activating, an internet web page, or a public device, wirelessly connected to the glasses, like, a multi-user door opener. The touch-free operation, enables the user to avoid bacteria, that may be on physical touch input buttons, of the public door opener.

The invention discloses a face recognition anti-fraud method, and relates to the technical field of face recognition. Comprising the following steps: acquiring a real face image and an attack sample image corresponding to the real face image; inputting the attack sample image into an image recognition model to obtain an image recognition result; training the image recognition model through a total loss function between the attack sample image and the recognition result to obtain a trained image recognition model; and inputting a face image to be recognized into the trained image recognition model to obtain an image recognition result, and applying the image recognition result to face anti-fraud recognition. According to the face recognition anti-fraud detection method, the generalization detection capability of diversified and forged attack sample images can be remarkably improved, and then the accuracy and the safety of face recognition anti-fraud detection are improved.

The invention provides a flexible facial privacy generative protection method, system equipment and medium, a model construction and training process and a model reasoning and generating process are performed, the model construction and training process constructs a network model, and the method comprises the steps of setting a pre-trained identity encoder to extract facial identity features, and setting a pre-trained identity encoder to extract facial privacy; setting a pre-trained attribute encoder to extract facial attribute features; inputting the identity feature vector and the attribute feature vector into a multi-layer perceptron for fusion to generate a high-dimensional semantic vector, taking the high-dimensional semantic vector as condition input of a diffusion model, and training the diffusion model; the model reasoning and generating process comprises the following steps: decoupling an input facial image to obtain identity and attribute feature vectors, and fusing the identity and attribute feature vectors into a high-dimensional semantic vector; random noise meeting differential privacy is injected into the identity feature vector to generate disturbance identity features; identifying a sensitive attribute direction and injecting noise to generate a disturbance semantic vector; and gradually denoising the noise image by using a diffusion model to generate a privacy protection image under the condition of a disturbed result.

A smart mirror system is provided, comprising a mirror, a camera, and a processor in communication with the camera and the mirror. In use, an image of a face is received for performing facial recognition. Based on the facial recognition, at least one of a plurality of users of the mirror is identified, for tailoring operation of the mirror for the at least one user.

The invention discloses an instant intention unlocking system based on facial recognition and voice interaction, and aims at solving the problems that an existing voice assistant depends on wake-up words, interaction is low in efficiency, scene perception is weak, and energy consumption is high. The system adopts a three-layer architecture including a hardware layer (a camera, a microphone, a processor and a storage module), a software layer (a voice recognition engine, a face recognition algorithm, a scene perception module and an authority control module) and a logic layer (an intention fusion judgment module and a task routing and execution module). 'gaze + voice 'is taken as core triggering, and wakeup words are avoided; face recognition is used for confirming identity and watching state, voice recognition is used for analyzing instructions, and an application scene is fused to generate an instant intention triggering model; authority hierarchical management and control are realized, and energy consumption optimization, auxiliary triggering, cross-terminal synchronization and emergency help modules are arranged. Interaction efficiency can be improved, safety and convenience are balanced, energy consumption is reduced, and the method is adaptive to multiple terminals and multiple scenes.

The invention provides a text guidance-based generative reverse face recognition method. The method comprises the following steps of: obtaining an initial latent vector and a fine tuning generator by using a pre-trained editing encoder and an original face image; taking the initial latent vector as an initial vector during gradient updating, and starting to circularly update until a final latent space code is obtained; and inputting the subsurface space code into the fine-tuned generator, and taking the generated image as a protected image corresponding to the original face image. The total loss function comprises an adversarial loss for explicitly promoting diversity and a loss function of a visual effect; the fine-tuned image generated by the generator is closer to an auxiliary image randomly selected from the auxiliary data set so as to realize protection of dynamic tracking of face recognition; meanwhile, it is ensured that the image generated by the fine-adjusted generator follows the specification of target text prompt and keeps the visual similarity with the original face image. The method has excellent performance in the aspect of preventing the face image of the user from being identified by the dynamic FR strategy.

The invention relates to the technical field of computer vision, in particular to a depth forgery detection method and system for priori knowledge guidance and double-domain representation optimization, and the method specifically comprises the following steps: obtaining a to-be-detected video containing a face identity; preprocessing the video data to be detected to obtain a face image; inputting the face image into a pre-trained deep counterfeiting detection model, and extracting deep counterfeiting evidence features of the image; the deep forgery detection model guides the feature extractor to learn potential knowledge for a real image in the pre-training model through priori knowledge, and establishes a perception ability for the real world; the robustness and generalization of the detection model are improved through an adaptive data augmentation mechanism of a feature hidden space and an image space; and the features are used as counterfeiting evidences for judging whether the facial image is deeply counterfeited or not. The method is lower in test cost, higher in efficiency and better in generalization, and can widely cope with deep counterfeit detection scenes.

The present disclosure belongs to the technical field of image processing. Provided are a video generation method and apparatus, and an electronic device and a computer program product. The video generation method comprises: extracting a facial identity feature of a target virtual human by using a facial recognition model; extracting an audio feature vector and an emotion prompt word from target audio by using an audio classifier; and using the audio feature vector and the emotion prompt word as control features, merging the control features with a diffusion model, using the facial identity feature to control the output of the diffusion model, and using the diffusion model to generate speech video data of the target virtual human, which speech video data matches the target audio. The technical solution of the present disclosure can generate a speech video stream of a virtual human with natural facial expressions.

The invention discloses a high-precision user facial feature extraction method based on multi-modal fusion, and belongs to the field of computer vision and multi-modal learning. In order to solve the problem of poor robustness of a single mode under weak light, shielding and attitude change, RGB / depth / near-infrared data is collected and subjected to time-space synchronization, coding is carried out through face detection and key point alignment, adaptive weight fusion is carried out according to quality by adopting interactive attention, loss optimization is matched with measurement learning, and a normalized feature vector is output. The method is used for facial recognition recommendation, living body verification and edge end real-time application.

To provide a face authentication system that does not require management by a management company or the like and allows residents alone to safely start using the system or change users. [Solution] The system comprises a face registration means for registering a user's face image or face data that is processed data thereof, a photographing device installed near the front door of a dwelling unit in an apartment building, a resident registration means for registering the user as a resident of the dwelling unit, an authentication means for comparing the face photographed by the photographing device with the resident's face data, and a resident tool that is a user interface for the face registration means and the resident registration means, and the user account for the resident tool is handed over to the resident of the dwelling unit along with the front door key, and the user account for the resident tool is provided by a face authentication system that is pre-registered for each dwelling unit in the apartment building.

The invention belongs to the technical field of artificial intelligence, and relates to a digital human action video generation method and device, computer equipment and a storage medium, and the method comprises the steps: collecting multi-dimensional data associated with a digital human; processing the multi-dimensional data to obtain target data; performing posture recognition on the target image information based on a human body posture recognition model to obtain posture data; performing facial recognition on the target facial information based on a facial recognition model to obtain facial data; analyzing the text conversion information and the target text interaction information based on an emotion semantic analysis model to obtain prediction label data; processing the posture data, the face data and the prediction label data based on an action library to obtain digital human action data; and rendering the digital human action data to obtain a digital human action video and displaying the digital human action video. In addition, the action library may be stored in a blockchain. The method can be applied to digital human action generation scenes in the medical science and technology field and the financial field, and the quality of the generated digital human action video is improved.

We provide the system. [Solution] A processing device that uses a facial recognition algorithm to detect human features from video data and compares them with a subject list, A device that monitors abnormalities in entrances and windows using a vibration detection unit and an opening / closing detection unit, A system that analyzes a person's behavior and facial expressions to quantify the degree of danger and issues a warning based on that evaluation, A means by which the information processing unit shares information about abnormal individuals with multiple external organizations, A means of transmitting a warning to a mobile communication device when an anomaly is detected, A system that includes this.

This application relates to the field of image recognition technology, specifically providing a method, apparatus, storage medium, and electronic device for training a facial recognition model. The method may include: inputting an image dataset for training into a backbone network module and outputting image feature information corresponding to the image dataset; inputting the image feature information into a feature processing module and outputting image fusion features of different sizes; inputting the image fusion features of different sizes into a target detection module and outputting image detection results; optimizing an initial facial recognition model using the loss value of the image detection results determined by a target loss function to obtain a trained facial recognition model; the facial recognition model is used to recognize facial expressions in the image to be recognized. Some embodiments of this application can improve the accuracy of the trained facial recognition model.

The invention provides, in some respects, a digital data processing method of identifying a person of interest in a video stream. The method, which executes on a digital data processing system including one or more digital data processing devices in communications coupling with one another, includes detecting using a detector and without using a facial recognizer one or more faces in a video stream. It further includes using a tracker to associate, without using a facial recognizer, one or more of the detected faces with a same apparent individual, and scoring, again, without using a facial recognizer, each of the detected faces that are associated with the same apparent individual. The method further includes applying selected ones of the scored detected faces of the same apparent individual to a facial recognizer to identify a person of interest, where selection is based on scores of the detected faces.

The present invention provides a system for assessing the state of consciousness of injured persons based on facial recognition, which relates to the field of health assessment technology. It includes a facial information extraction module, a facial feature analysis module, a state of consciousness calculation module, an anomaly detection module, and a state of consciousness assessment module. In the present invention, an infrared camera is used to carefully capture the subtle movements of the muscles around the eyes, corners of the mouth, and forehead, thereby improving the accuracy and comprehensiveness of data collection. By evaluating the blinking frequency, displacement of the corners of the mouth, and stretching amplitude of the forehead muscles, the accuracy and dimension of the assessment are improved. By extracting deformation features and calculating the gradient of motion amplitude, combined with time series analysis, the assessment of the state of consciousness becomes more sensitive and accurate, and is able to detect potential changes in consciousness in a timely manner, thereby responding quickly. The density and distribution feature analysis of mutation points provide rich data interpretation for diagnosis and monitoring, so that the state of consciousness of the injured person can be quickly and accurately assessed without contacting the injured person.

The present invention relates to an in-vehicle rotating screen with automatic face recognition adjustment in the field of in-vehicle display devices. A camera unit is integrated on the display screen. The camera unit collects face information and outputs a signal to the face recognition module. The face recognition module performs user face recognition and face information entry on the input signal of the camera unit. At the same time, the face recognition module transmits the face information to the single-chip microcomputer control module. After calculation, the single-chip microcomputer control module sends a signal to the motor drive module in real time. The motor drive module is used to drive Motor 1 and Motor 2 to achieve two-degree-of-freedom rotation of the display screen. At the same time, the best display effect is given according to the distance information of the face. By performing face recognition on the driver, the present invention realizes automatic rotation adjustment in cooperation with the driver, facilitating the driver to view the screen information or operate the screen preferentially, and the display screen has a stable posture during driving, which can improve driving safety.

The invention discloses a configuration and use method of an anti-cheating alcohol detection system. The configuration and use method comprises the following steps: S11, prefabricating a detector; s12, prefabricating a vehicle control mechanism; s13, pre-storing user data; s21, the vehicle is powered on; s22, confirming the identity of the user; and S23, alcohol detection, wherein the face recognition module keeps face recognition during alcohol detection. According to the configuration and use method of the anti-cheating alcohol detection system provided by the invention, the face recognition module matches the identity of a user before the detection process of the alcohol detection module, and continuously recognizes the face data of a driver in the alcohol detection process; it is ensured that detection data of the alcohol detection module is matched with face data recognized by the face recognition module, a vehicle ignition system is directly locked or unlocked according to a detection result, and drunk driving is stopped from the source.

A secure identity verification method according to the present invention comprises: generating a secure work area when acquisition of a face image and an identification card image from an edge terminal is detected; extracting the face image and the identification card image from encrypted data received from the edge terminal in the generated secure work area; checking the degree of matching between feature data in a face area of the extracted face image and feature data in a face area of a face photograph included in the identification card image; sealing set data including the feature data for the face image and the identification card image confirmed to match and character string data in the identification card with a sealing key and transmitting same to storage; and when identity authentication is requested from a specific edge terminal, receiving the sealed set data corresponding to the face image received from the edge terminal from the storage, unsealing the set data, and comparing feature data extracted from the face image with the unsealed set data to perform identity verification.

Embodiments of the present application disclose a face recognition method, device, equipment and medium. The method comprises: obtaining a to-be-recognized face image; wherein the to-be-recognized face image comprises a to-be-recognized near-infrared image and / or a to-be-recognized visible light image; inputting the to-be-recognized face image into a trained face recognition model to obtain a face recognition result; wherein the face recognition model is trained based on near-infrared sample images and visible light sample images of sample training objects. The above scheme uses the face recognition model trained based on the near-infrared sample images and the visible light sample images to perform face recognition, which is convenient to operate, can simultaneously perform face recognition on the to-be-recognized near-infrared image and the to-be-recognized visible light image, and avoids the case that the face recognition accuracy is not high due to the influence of environmental factors or the lack of detailed texture information when face recognition is performed according to a single to-be-recognized face image, thereby improving the face recognition accuracy.

A computing system is provided, including one or more processors and associated memory storing instructions that cause the one or more processors to execute a social media program. The processor is configured to recognize, via a face recognition module of the social media program, one or more faces in the video. The processor is configured to cause a graphical user interface (GUI) to be displayed. The GUI includes a face selection tool that is configured to enable a user to select at least one recognized face to be masked. The processor is configured to mask the selected at least one recognized face in the video, to thereby generate an edited video. After masking, the processor is configured to share the edited video to a social media platform for viewing by other users.

1. Name of the product of this design: Graphical user interface for a facial recognition module of an electronic device. 2. Purpose of this design product: for use in an electronic device. 3. The key design point of this design product lies in the graphical user interface. 4. The picture or photo that best illustrates the key points of the design: main view. 5. Purpose of the graphical user interface: an operating interface for entering facial features. 6. Human-computer interaction method of the graphical user interface: After entering the facial recognition module, the main view is displayed. Click the "Confirm" button in the main view to display the interface change state diagram 1. Click the "Start Entry" button in the interface change state diagram 1 to display the interface change state diagram 2. When the facial entry is completed, the interface change state diagram 3 is displayed.

The invention provides a facial feature recognition method and device based on semi-supervised pre-training, and relates to the technical field of facial recognition, and the method comprises the steps: generating a face segmentation data set based on a face image set in a target scene; dividing the face segmentation data set into a supervised sample data set and a non-supervised sample data set for performing semi-supervised pre-training on an identification backbone network in the facial feature identification network, freezing parameters of the identification backbone network, and utilizing a pseudo tag of the non-supervised sample data set and the supervised sample data set to identify the facial feature identification network; and training the multi-subclass prediction head in the facial feature recognition network to obtain a target facial feature recognition network, the target facial feature recognition network being used for predicting facial features of the target object. According to the method, the problems of high data labeling cost and low accuracy of a trained model of face feature recognition in a medical scene can be solved.

A ticketless system and method for mass attendee event venue entry and item delivery system for facilitating expeditious and controlled entry of an influx of people to the event without overwhelming event venue employees or resources. A series of pass-through images of group of people is taken by a camera (106) as the group moves through a photo zone (124). The at least one of the images is compared using facial recognition technology with a stored profile image of a person from the group to identify the person and confirm authorized entry into the event without the need for the person to present a ticket or pass for the particular event. The same entry image may be used to effectuate multiple determinations, including one or more a security determination, and procurement and delivery of any item pre-selected by the person directly to the person's allocated seating.