693 results about "Facial region" patented technology

Systems, methods, and non-transitory computer readable media including instructions for interpreting facial skin micromovements are disclosed. An example head mountable system includes a housing configured to be worn on a head of a wearer. The head mountable system also includes at least one detector integrated with the housing and configured to receive light reflections from a facial region of the head and to output associated reflection signals. The head mountable system also includes at least one microphone associated with the housing and configured to capture sounds produced by the wearer and to output associated audio signals. The head mountable system also includes and at least one processor in the housing, configured to use both the reflection signals and the audio signals to generate output that corresponds with words articulated by the wearer.

An example computer-implemented image processing method includes receiving a digital image including a face area of a first subject and a background area, receiving a reference image of a second subject, modifying the digital image based on the reference image utilizing a generative neural network to create a modified digital image in which a feature of the second subject is transferred to the first subject, and combining the face area of the first subject and the background area from the digital image with the feature area from the modified digital image to create a composite modified digital image that includes data representing the feature of the second subject transferred to the first subject from the modified digital image and data representing the face area of the first subject and the background area from the digital image.

Systems and methods are disclosed that animate a source portrait image with motion (i.e., pose and expression) from a target image. In contrast to conventional systems, given an unseen single-view portrait image, an implicit three-dimensional (3D) head avatar is constructed that not only captures photo-realistic details within and beyond the face region, but also is readily available for animation without requiring further optimization during inference. In an embodiment, three processing branches of a system produce three tri-planes representing coarse 3D geometry for the head avatar, detailed appearance of a source image, as well as the expression of a target image. By applying volumetric rendering to a combination of the three tri-planes, an image of the desired identity, expression and pose is generated.

A novel multi-modal audio video framework is disclosed. The framework advantageously leverages both audio and video information to accurately detect whether the video has been manipulated, e.g., detect whether the video is a so-called ‘deepfake.’ In a video-only pipeline, the framework adopts a vision encoder having a feature extractor and a Transformer encoder that leverages self-attention mechanisms to detect artifacts in a facial region of the video. Additionally, in a separate audio-video pipeline, the framework adopts an audio+lip encoder having a Transformer encoder that leverages cross-attention mechanisms to identify discrepancies between lip movements of the person and words spoken by the person in the video. These two modalities are used jointly to make an inference as to whether the video has been manipulated.

The invention relates to a virtual image expression generation method and system based on a real feeling technology. The method comprises the steps of collecting facial expression data in a multi-angle mode, and performing triangulation and facial region marking through the collected facial expression data; component analysis is carried out on the facial region, main features of facial expression changes are extracted, and a facial model is established through weighted fusion; establishing a mapping relation between the expression features and the facial displacement, and calculating a geometric constraint function of the facial displacement; correspondingly generating different expression models according to the geometric constraint function, optimizing details of a specific area and realizing dynamic face rendering on the virtual image; by introducing multi-view data acquisition, a multi-level feature extraction and fusion strategy, expression displacement regression analysis, geometric constraint and detail optimization, the authenticity and personalized effect of virtual expression generation are greatly improved.

The invention discloses a face video style migration method and system, and belongs to the technical field of computer vision, and the method comprises the steps: inputting an obtained face video frame image into a constructed generator network model, carrying out the affine transformation of a feature sequence of the face video frame image outputted by an encoder of the model, and obtaining a face video style; fusing the feature sequence into a target style feature through a transformation function; constraining the features of the face region of the target style feature on the deformation protection layer according to the face mask, and obtaining stylized face frame image features generated by the current condition reversible residual block; and inputting the stylized face frame image features generated by the current conditional reversible residual block into the next conditional reversible residual block in sequence for loop execution, and decoding through a decoder to obtain a stylized face frame image fused by the plurality of conditional reversible residual blocks. The video generated by the method is remarkably improved in the aspects of style consistency, facial detail retention and time sequence coherence, and high-quality face video style migration is realized.

Systems, methods, and non-transitory computer-readable media containing instructions are disclosed for detecting and utilizing facial skin micro-motion. In some non-limiting embodiments, detection of facial skin micro-motion occurs using a speech detection system that may include a wearable housing, a light source (coherent light source or incoherent light source), a light detector, and at least one processor. The one or more processors may be configured to analyze light reflections received from the facial region to determine facial skin micromotion, and extract meanings from the determined facial skin micromotion. Examples of meanings that may be extracted from the determined facial skin micromotion may include words spoken by the individual (silent or voiced), an identity of the individual, an emotional state of the individual, a heart rate of the individual, a respiratory rate of the individual, or any other biometric, emotion or speech related indicator.

To appropriately perform screen brightness reduction processing according to the orientation of a face even when an external display is connected.SOLUTION: An information processing apparatus includes: a memory that temporarily stores image data on an image captured by an imaging portion; a processor that processes the image data stored in the memory; an internal display; and a connection portion that connects to an external display. The processor performs: face detection processing of processing the image data on the image stored in the memory to detect a face area, in which a face is captured, and an angle of the face from the image; and brightness reduction processing of reducing screen brightness of either one or both of the internal display and the external display when the angle of the face detected by the face detection processing is out of a preset angle range. A setting of the angle range used in the brightness reduction processing is different between a state where the external display is not connected to the connection portion and a state where the external display is connected to the connection portion.SELECTED DRAWING: Figure 7

The invention provides a screen interaction method and system based on face recognition, and the method comprises the steps: obtaining a face image sequence triggered by a target user in a screen interaction scene, carrying out the matching of a target interaction mode from a preset interaction mode library according to the face dynamic behavior characteristics, and carrying out the matching of a target interaction mode based on the coordinates of a screen fixation region and the target interaction mode. Generating dynamic interaction content corresponding to the sight focus of the user, calling a pre-trained facial feature fusion model, performing multi-level feature extraction on a facial region in the facial image sequence, generating a user identity verification vector and an emotional state vector, and performing real-time adjustment on presentation parameters of the dynamic interaction content based on the multi-level feature extraction. And outputting the adjusted screen interaction signal. According to the invention, the user experience can be optimized while the interaction efficiency is improved.

The invention discloses a monocular video face reconstruction and driving method based on Gaussian splashing and a 3DMM model. The monocular video face reconstruction and driving method comprises the following steps: S1, converting each frame of a face RGB video into an explicit expression face grid based on the 3DMM model; s2, constructing a geometric enhancement module, and carrying out geometric alignment on eye and tooth regions by adopting an improved iterative nearest point algorithm; s3, initializing a 3D Gaussian ball, and driving the dynamic change of the 3D Gaussian ball according to the point change of the face grid under different expressions; s4, based on the characteristics of different face areas, adaptive optimization learning strategy coefficients are distributed; and S5, increasing confidence information for each 3D Gaussian ball, generating a confidence image through rendering, multiplying the confidence image by a rendered image, calculating high-dimensional feature similarity with the input image, and optimizing attribute parameters and rendering effects of the 3D Gaussian balls. According to the method, Gaussian splashing and a 3DMM model are combined, and high-precision reconstruction and driving of a monocular video face are realized.

The invention discloses an identification photo generation method and electronic equipment, and relates to the field of image processing, after an original user image and an original template image (identification photo template) aiming at the original user image are acquired, image deformation is carried out on the original template image according to the original user image and an image deformation algorithm, and the identification photo is generated. A target template image which aims at an original user image and conforms to one or more of facial key point constraints, light and shadow consistency constraints and facial structure constraints is obtained, and the original user image and the target template image are input into a trained image generation model (obtained by optimizing and training a diffusion algorithm); the corresponding areas are processed according to the different processing weights corresponding to the face area and the non-face area, it is ensured that the obtained target identification photo is more natural, real and reliable in vision, the specification requirement of an actual scene for the identification photo is met, the calculation time and cost are reduced, and the requirement for large-scale identification photo batch generation is met.

The invention discloses a face posture recognition method, an anti-dazzle lamp regulation and control method, a lamp, equipment and a medium, and relates to the technical field of lamp control. According to the method, firstly, face region recognition is performed based on the integral image corresponding to the video image, then the face image is intercepted from the video image according to the recognition result, and then face pose recognition is performed based on the trained CNN model and the trained backbone network model, so that recognition of each face pose in the target scene is realized, and the recognition efficiency is improved. And then the irradiation angle and brightness of the lamp are adjusted based on the recognized face posture, and adaptive adjustment of the glare problem is achieved.

The embodiment of the invention provides a generation method and generation equipment of a digital human interaction video. In the method, after a generation device obtains a target voice, a basic video and a facial image set can be obtained from a pre-constructed material library, key phoneme switching information corresponding to the target voice can be obtained by mapping each original phoneme in the target voice into one of a plurality of key phonemes, and in addition, the key phoneme switching information corresponding to the target voice can be obtained. Based on the facial image set and the key phoneme switching information, a target facial image sequence matched with the phoneme change condition in the target voice can be generated, and then the target facial image sequence is attached to the facial region of the digital human in the basic video to obtain a target video.

The invention relates to the technical field of drunk driving detection, and discloses a non-contact drunk driving detection method based on deep learning, which comprises the following steps: acquiring continuous video stream data through a multispectral camera, extracting a facial region image sequence after inter-frame alignment and noise suppression, and extracting a facial region image sequence; feature extraction and fusion are carried out by using a space-time convolutional neural network, a time sequence decomposition algorithm and the like; and finally, a drunk driving detection result is obtained through a cascaded deep learning classifier and a dynamic threshold segmentation algorithm. The system comprises a data acquisition module, a preprocessing module, a feature extraction module, a classification decision module and a model optimization module. According to the invention, non-contact detection is realized, the risk of cross infection is avoided, and the detection convenience is improved. Various advanced algorithms adopted by the method can accurately extract characteristics related to alcohol metabolism, optimize model performance, improve detection precision and reduce misjudgment caused by environmental factor interference, and the method also has the functions of data enhancement, multi-task learning and online incremental learning, adapts to different environments and guarantees road traffic safety.

The invention discloses a head reconstruction method based on a single photo, and the method employs a high-low model mesh fusion algorithm to guarantee the facial details, achieves the model lightweight, and guarantees that the model can adapt to a mobile device and meet the real-time rendering demands. Through combination of key point detection and a PnP algorithm, accurate projection and extraction of facial textures are realized, and the fidelity of texture details is effectively improved. Besides, a skin color matching method based on a Lab color space is introduced, the problem of chromatic aberration between the face area and the prefabricated chartlet is solved, natural edge transition and vivid visual effect are ensured, and therefore a high-quality head three-dimensional model meeting the requirements of multiple platforms and multiple scenes is generated.

Systems, methods, and non-transitory computer-readable media including instructions for detecting and utilizing facial skin micromovements are disclosed. In some non-limiting embodiments, the detection of the facial skin micromovements occurs using a speech detection system that may include a wearable housing, a light source (either a coherent light source or a non-coherent light source), a light detector, and at least one processor. One or more processors may be configured to analyze light reflections received from a facial region to determine the facial skin micromovements, and extract meaning from the determined facial skin micromovements. Examples of meaning that may be extracted from the determined facial skin micromovements may include words spoken by the individual (either silently spoken or vocally spoken), an identification of the individual, an emotional state of the individual, a heart rate of the individual, a respiration rate of the individual, or any other biometric, emotion, or speech-related indicator.

The invention provides a video coding method and device, electronic equipment and a storage medium, relates to the technical field of image processing, in particular to the field of video coding and the like, and can be applied to application scenes such as video live broadcast and the like. The specific implementation scheme is as follows: acquiring a reference frame which is adjacent to a current frame and has established initial ROI hierarchical division; multiplexing a motion vector generated by the reference frame in a motion compensation time domain filtering process, and determining an ROI position predicted value of the current frame; extracting feature points of the five sense organs of the current frame, and correcting an ROI position predicted value based on a feature point spatial topological relation in the initial ROI; respectively configuring differential quantization parameters for the corrected main face region, the corrected secondary face region and the corrected background region; dynamically allocating a three-layer area code rate according to a real-time network bandwidth; and outputting the coded frame of the current frame and the associated ROI level metadata. According to the scheme, the coding efficiency and quality can be improved.

The invention relates to a high-definition monitoring camera tracking method and system for multi-angle face detection. The method comprises the following steps: preprocessing an original image collected by a high-definition monitoring camera to obtain a first preprocessed image and a second preprocessed image; performing feature extraction and feature fusion on the first preprocessed image and the second preprocessed image to obtain a multi-scale fusion feature map; face positioning and posture analysis are carried out through a position sensitive convolutional network, and a face detection result is obtained; performing face region subdivision and angle characteristic analysis on the face detection result to obtain enhanced face representation; and calculating a cross-camera matching cost matrix based on the enhanced face representation, and performing trajectory prediction and updating processing to obtain a face tracking result in the multi-camera environment. According to the method, the capability of capturing facial micro-expressions and fine features is improved, high-precision face association and smooth trajectory handover among different cameras are realized, and the problem of trajectory fragmentation in view crossing areas of the cameras is solved.

The invention provides a pulse wave signal (BVP signal) extraction method based on remote photoplethysmography. The method comprises the following specific steps: step 1, acquiring a face video and detecting a face area; in the process, the system captures, recognizes and extracts all facial feature points; 2, the rPPG signal intensities of different face areas are different, and in order to maximize the signal quality, the face areas are divided according to the intensities of remote photoplethysmography signals (rPPG signals); 3, training a convolutional neural network model, calculating a weighting coefficient of each region according to the characteristics of the signals of each region, and fusing the signals of each region into a single-channel signal; 4, removing low-frequency noise and interference signals from the fused signals by using a filtering technology, wherein the superposition operation helps to enhance the effective rPPG signals; through the processing steps, the quality of the BVP signals can be improved, and high-quality input data is provided for subsequent extraction of blood oxygen, respiration, heart rate variability and other signals.

The invention provides a wearable safety system (100; 100 ') configured for use in combination with a helmet (200); 100 '). The system comprises a strap (1; 1 ') comprising two shoulder straps (3a, 3b); at least one inflator (10a, 10b), an ECU (13) and a battery (14). The ECU (13) is arranged in communication with the at least one inflator (10a, 10b). The system further comprises at least one airbag (9a, 9b). The system is configured to generate an enable signal upon detection of an accident. The at least one bladder (9a, 9b) is configured to be inflated upon generation of the activation signal and to be deployed to form at least one inflated C-shape covering a portion of a face region (302) and a neck portion (301) of the wearer. The invention also relates to a bag arrangement.

This invention belongs to the field of facial expression recognition and provides a micro-expression recognition method based on convolutional neural networks and optical flow features, which is used to improve the adaptability and accuracy of micro-expression recognition. The method first constructs a face detection module to perform face detection on each image frame in a video segment, outputs facial key point information, and extracts facial regions of interest (ROIs) based on the facial key point information: nose, mouth, left eye and eyebrow, right eye and eyebrow. Then, optical flow information and optical strain information are introduced to characterize the spatiotemporal information of facial movement and the intensity information of facial deformation. The optical flow eigenvalue of each image frame is calculated, and a binary search algorithm is used to search for the image frame with the maximum optical flow eigenvalue, which is used as the vertex frame for micro-expression recognition. Finally, a micro-expression recognition model based on a convolutional neural network is constructed, which uses the horizontal and vertical components of the optical flow and optical strain of the vertex frame as input. The micro-expression recognition model outputs a micro-expression category prediction result.

An information processing apparatus performs face detection processing to detect a face area and the orientation of a face from an image captured by an imaging unit, brightness reduction processing in which, when the orientation of the face detected by the face detection processing changes from a first orientation to a second orientation while the screen brightness of a display unit is controlled to a first screen brightness, the screen brightness is reduced from the first screen brightness to a second screen brightness after first time has elapsed, and imaging state determination processing to determine whether an imaging state is a first imaging state where the imaging unit can capture an image in an image brightness capable of detecting the face area or a second imaging state where the imaging unit cannot capture any image in the image brightness capable of detecting the face area.

Provided are: a covered area determination unit that determines whether or not a face area including a face of a target person captured in an image generated by imaging the target person includes a covered area that covers at least partial area of the face; a first determination unit that determines whether or not the target person is a living body by a first determination method using the image, in a case where the covered area determination unit determines that the face area does not include the covered area; and a second determination unit that determines whether or not the target person is a living body by a second determination method that uses the image and that is different from the first determination method, in a case where the covered area determination unit determines that the face area includes the covered area.

A method for generating treatment recommendations based on images of a facial region of a human subject comprises the steps of acquiring at least one image of the facial region and generating image data representing the at least one image; processing the image data and additional information, including at least an indication of a condition affecting an appearance of the facial region and a treatment type to treat the condition, to obtain a treatment recommendation and outputting the obtained treatment recommendation. The processing step includes a comparison substep to compare information derived from the image data with reference information to obtain a reference measure. The processing step further includes a classifying substep to obtain the treatment recommendation (63.1 . . . 8, 64.1 . . . 8) based on the reference measure.

Systems, methods, and non-transitory computer-readable media including instructions for detecting and utilizing facial skin micromovements are disclosed. In some non-limiting embodiments, the detection of the facial skin micromovements occurs using a speech detection system that may include a wearable housing, a light source (either a coherent light source or a non-coherent light source), a light detector, and at least one processor. One or more processors may be configured to analyze light reflections received from a facial region to determine the facial skin micromovements, and extract meaning from the determined facial skin micromovements. Examples of meaning that may be extracted from the determined facial skin micromovements may include words spoken by the individual (either silently spoken or vocally spoken), an identification of the individual, an emotional state of the individual, a heart rate of the individual, a respiration rate of the individual, or any other biometric, emotion, or speech-related indicator.

An electronic device and method for face detection based on facial key-points is provided. The electronic device receives an image of an object of interest. The image may include a face of the object of interest. The electronic device detects a plurality of key-points associated with the face of the object of interest based on the received image and determines a first coordinate value in the received image based on the detected plurality of key-points. Thereafter, the electronic device determines a region in the received image that includes the face of the object of interest based on the determined first coordinate value and controls the display device to overlay a marker onto the determined region of the face. The marker indicates a location of the determined region in the image.

The invention provides a non-contact heart rate monitoring method and system based on adaptive feature fusion and key region division. The method comprises the steps that firstly, a face detection data set is obtained, a multi-scale feature extraction network is constructed, input face video frames are processed in parallel through convolution layers with different convolution kernel sizes, and multi-scale feature extraction is completed; designing an AFFM adaptive feature fusion module, using a SE-Net channel attention mechanism to obtain channel and space attention weights, fusing feature maps with different scales, and enhancing feature expression to obtain a feature map for face detection; then selecting a face key region, delimiting a region boundary by means of a deep learning semantic segmentation model, extracting light intensity information of each region at different moments, and integrating through a linear weighted fusion model to obtain a comprehensive heart rate signal source; and finally, denoising by using a filter technology, and detecting a light intensity peak value in combination with a sliding window so as to obtain a heartbeat cycle and a heart rate value.

The invention relates to the technical field of pressure control, in particular to a prone position surgery face pressure adjusting method and system based on pressure induction, and the method comprises the following steps: based on an embedded miniature pressure sensor, detecting the pressure value of a face key area, analyzing the pressure distribution condition, and calculating the face pressure value; the concentrated stress state of each area of the face is analyzed through the pressure value change trend, and face area pressure distribution trend data are obtained. In the invention, through a pressure sensing technology and a dynamic adjustment mechanism, the problem of uneven pressure of the face in a prone position operation can be effectively identified and relieved, pressure distribution can be monitored and analyzed in real time, the pressure of a high-sensitivity area is accurately adjusted to be transferred to a low-sensitivity area, uniform distribution is realized, tissue damage caused by local long-term compression is reduced, and the operation safety is improved. The support force of each pressed point is balanced, the face pressure adjustment is optimized, the distribution stability is improved through pressure fluctuation monitoring and correction, the non-uniform phenomenon caused by fluctuation is avoided, and the surgical comfort and safety are improved.

The invention discloses a voice-driven speaking portrait generation method and electronic equipment. The method comprises the following steps: acquiring an audio feature, a spatial feature and an image feature; inputting the audio features and the spatial features into an audio-spatial coding module, and aligning the audio features with the spatial features by dynamically adjusting the spatial positions of the audio features and the face region to obtain spatially perceived lip features; inputting the audio features and the image features into an audio-guided dynamic fusion module, and calculating audio-perceived channel attention and space attention by using a dynamic linear layer to obtain dynamic fusion features; obtaining reference key points, inputting the reference key points and the audio features into an expression optimization module, and fusing the reference key points and the audio reference points to obtain key point features; and inputting the lip sensing feature, the dynamic fusion feature and the key point feature into a trained generative network to generate a video frame. According to the method, the higher-quality, more natural and stable voice-driven portrait synthesis effect is achieved.

A facial image replacement method and apparatus, and a device, a storage medium and a program product. The method comprises: acquiring a first sample image, a second sample image and a sample replacement image (210); using sample noise data to perform n instances of noise addition on the sample replacement image in a time dimension, so as to obtain a sample noise-added image (220); in the process of performing facial area replacement on the first sample image and the second sample image by means of a facial image replacement model, performing prediction on the basis of the sample noise-added image, so as to obtain predicted noise data (230); and using the difference between the sample noise data and the predicted noise data to train the facial image replacement model, so as to obtain a trained facial image replacement model (240). By means of the above manner, a facial replacement process can be implemented while removing noise, thus solving the problem of the facial replacement generation effect being relatively poor caused by the relatively low image definition, and improving the robustness of a trained facial image replacement model, which can be applied to various scenarios such as cloud technology, artificial intelligence and smart traffic.