375 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.

The invention relates to the technical field of medical cosmetology and computer vision, in particular to a 3D skin measurement method based on high-precision measurement of a face concave-convex degree region, and the method comprises the following steps: arranging a structured light camera to collect a face RGB image and depth data, and carrying out the filtering and denoising to generate a smooth point cloud; detecting an RGB image based on FaceMesh to obtain face key points, and constructing a multi-region segmentation algorithm; segmenting an RGB local area and converting the RGB local area into point cloud data; iDW interpolation is carried out to generate a dense point cloud, and a two-dimensional contour line is extracted through a Marking Square algorithm; and mapping the color of the matched color card to an image area to generate a contour map. According to the method, the face recognition model is constructed through the FaceMesh architecture, accurate positioning of multiple face types is adapted, two-dimensional contour mapping is adopted to three-dimensional contour mapping, data are intercepted, local processing is combined, the operation load is reduced by 60%, and efficiency is remarkably 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.

A computer-implemented method of changing a face within an output image or video frame that includes: receiving an input image that includes a face presenting a facial expression in a pose; processing the image with a neural network encoder to generate a latent space point that is an encoded representation of the image; decoding the latent space point to generate an initial output image in accordance with a desired facial identity but with the facial expression and pose of the face in the input image; identifying a feature of the facial expression in the initial output image to edit; applying an adjustment vector to a latent space point corresponding to the initial output image to generate an adjusted latent space point; and decoding the adjusted latent space point to generate an adjusted output image in accordance with the desired facial identity but with the facial expression and pose of the face in the input image altered in accordance with the adjustment vector.

A method includes obtaining image frames capturing one or more people in at least one scene and identifying features of the image frames. The method also includes providing the identified features to a trained spatiotemporal transformer machine learning model configured to generate a set of features for each of the one or more people. The set of features for each person includes facial features of the person and pose features of the person over time. The method further includes performing face identification using the facial features to generate one or more first embeddings representing at least one face of at least one person and performing gait identification using the pose features to generate one or more second embeddings representing at least one gait of at least one person. In addition, the method includes identifying at least one of the one or more people based on the first and second embeddings.

The invention relates to a student learning state monitoring method based on facial recognition mental stress analysis. Comprising the following steps of A, environment acquisition and equipment installation, B, time and frequency acquisition, C, face monitoring and tracking, D, region-of-interest selection, E, rPPG signal extraction, F, feature extraction, G, pressure analysis and H, student pressure level monitoring, and the pressure analysis in the step G is applied to actual student learning state monitoring in the step H. Acquiring facial image data of the student in real time, performing mental stress analysis, regularly evaluating the performance of the optimized stress evaluation model, and updating and improving the optimized stress evaluation model according to new data and practical application feedback; the method has the advantages that the mental stress level of a student is evaluated by analyzing the facial expression of the student; facial expression data of students can be obtained in real time, and instant state changes of the students in the learning process can be reflected in time.

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.

A computerized method of generating training and / or testing data for a face recognition machine learning network applied by a face recognition system is presented. The method comprises defining a semantic parameter space, wherein the semantic parameter space comprises a vector of semantic parameters associated with a facial image. The method further comprises training a mapping machine learning network to transform the vector of semantic parameters to a vector of style space parameters of a style-based generative adversarial network. When the mapping machine learning network is trained, the method further comprises generating a variation vector of style space parameters for an input facial image by applying the trained mapping machine learning network and feeding the style-based generative adversarial network with the variation vector of style space parameters to generate a variation facial image for the input facial image. Finally, the method comprises storing the variation facial image in a database and training and / or testing the face recognition machine learning network at the face recognition system by using a plurality of variation facial images stored in the database. A corresponding face recognition system and a computer program are provided, too.

The invention belongs to the technical field of face recognition, and provides a face recognition system and an anesthesia cabinet using the same, and the system comprises an image collection module which is used for obtaining the face image data of a to-be-stored user through face detection; the feature extraction module is used for acquiring a planar graph of the facial image of the to-be-stored user through the facial image data of the to-be-stored user; the calculation module is used for subdividing the left eye region and the right eye region of the user to be stored through a loop subdivision algorithm; the face recognition module is used for performing face detection through the recognition module to obtain face image data of the to-be-detected user when the to-be-detected user needs to open the anesthetic cabinet; and the judgment module is used for judging whether the anesthetic cabinet is opened. According to the face recognition system provided by the embodiment of the invention, face information can be directly and accurately recognized through eyes in a medical environment.

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..

The embodiments execute machine-learning architectures for biometric-based identity recognition (e.g., speaker recognition, facial recognition) and deepfake detection (e.g., speaker deepfake detection, facial deepfake detection). The machine-learning architecture includes layers defining multiple scoring components, including sub-architectures for speaker deepfake detection, speaker recognition, facial deepfake detection, facial recognition, and lip-sync estimation engine. The machine-learning architecture extracts and analyzes various types of low-level features from both audio data and visual data, combines the various scores, and uses the scores to determine the likelihood that the audiovisual data contains deepfake content and the likelihood that a claimed identity of a person in the video matches to the identity of an expected or enrolled person. This enables the machine-learning architecture to perform identity recognition and verification, and deepfake detection, in an integrated fashion, for both audio data and visual data.

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.

Describes the use of multimodal facial attributes in a facial recognition system. Additionally, the use of one or more auxiliary attributes, such as temporal attributes, can be combined with visual information to improve the facial recognition performance of the facial recognition system. In some examples, the use of multimodal facial attributes in a facial recognition system can be combined with the use of one or more auxiliary attributes, such as temporal attributes. Each of these techniques can improve the verification performance of the facial recognition system.

The invention relates to the technical field of detection, in particular to a self-supervision-based lightweight dynamic facial expression detection method and system, and the method comprises the steps: obtaining a training image from a video set with a facial image, and constructing and training a self-supervision neural network model based on the training image, thereby achieving the detection of the facial expression. The facial expression recognition method comprises the steps of obtaining a facial recognition self-supervised neural network model, processing a dynamic image, and inputting the processed dynamic image into the facial recognition self-supervised neural network model, thereby outputting a facial expression recognition result, and realizing the recognition and detection of the dynamic facial expression.

The invention provides a payment processing method and device based on facial recognition, equipment and a medium, relates to the technical field of artificial intelligence, can be applied to scenes such as cloud technology, artificial intelligence, smart traffic and aided driving, and comprises the steps of responding to a facial recognition payment request of a target object; acquiring image data acquired by image acquisition equipment and audio data corresponding to the image data; performing feature analysis on the audio data according to the number of personnel to obtain an audio analysis result; if the audio analysis result indicates that the number of persons corresponding to the audio data is a single person, performing face recognition on the image data based on a face recognition model corresponding to a single person scene to obtain target face data of the target object; and performing payment operation corresponding to the facial recognition payment request based on the target facial data. According to the invention, the redundant time consumption of payment identification can be effectively reduced.

The invention discloses a fatigue driving intervention method, device, system and equipment based on facial recognition, and the method comprises the steps: obtaining a facial image sequence of a driver, and determining a corresponding local region image sequence; the facial image sequence and the local area image sequence are input into a preset facial expression recognition model, the facial expression category of the driver is obtained, in the preset facial expression recognition model, a local feature extraction module is used for conducting feature extraction on the local area image sequence based on a long-short-term memory neural network block, and the facial expression category of the driver is obtained; obtaining local dynamic features; the global feature extraction module is used for performing feature extraction on the facial image sequence based on deformable convolution to obtain global appearance features; and determining fatigue driving intervention measures according to the facial expression type and generating a corresponding execution instruction. According to the invention, the preset facial expression recognition model can accurately recognize the facial expression category of the driver in the changing scene, so that the timely response and execution of the fatigue driving intervention measure are realized.

A face identification system, and a non-transitory computer readable medium with instructions, that when executed, perform a method for identifying a face image are described. The face identification system comprises a camera for facial image capture, encryption circuitry for image encryption, feature extraction circuitry for processing encrypted images, a face recognition system for identification, and an identification notifying system for result transmission. The encryption circuitry includes a pixel-bit converter, transforming pixel intensities into binary digits, and a 2D cellular automata function, working with a 3×3 matrix of binary-state cells transitioning by update rules. A gray code generator maps binary numbers to unique gray-scale values. The system integrates advanced encryption techniques with efficient feature extraction and recognition algorithms, addressing challenges in biometric authentication. This approach enhances security, accuracy, and computational efficiency compared to conventional systems, making it suitable for various applications requiring secure and efficient facial recognition.

The present application is applicable to the field of image processing technology, and provides a training method for a facial image restoration model. The training method includes: obtaining training samples and corresponding facial key point sample images; inputting the facial sample images and the facial key point sample images into an initial facial image restoration model, so as to trigger a generation network to predict a restored sample image corresponding to the facial sample image based on the facial identity features of the facial sample image constrained by a first feature extraction network and the facial key point features of the facial key point sample image constrained by a second feature extraction network; training the initial facial image restoration model based on the restored sample image and a restored reference image to obtain a target facial image restoration model, and the target facial image restoration model is used to perform facial image restoration on a to-be-restored facial image. High-quality facial image restoration can be achieved through the present application.

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.

Disclosed is an electronic device for correcting an error during recognition of a gaze direction. The electronic device includes: a camera module including a camera configured to generate an image frame, a memory, and a processor, and the processor may be configured to: perform face recognition with respect to the image frame, perform extraction of a feature point of the face in response to the face being recognized during the face recognition, recognize a face direction of the face in response to the feature point of the face being extracted, generate gaze direction information by recognizing a gaze direction of the face in response to the recognition of the face direction, generate filtered gaze direction information by performing filtering with respect to the gaze direction information in response to the gaze direction being recognized, and store the filtered gaze direction information in the memory.

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.

The embodiment of the invention discloses a data processing method, device and equipment. The method comprises the following steps: receiving a facial image input by a user and used for facial recognition; the facial image is input into a facial expression editing model, edited images carrying expressions corresponding to the facial image are obtained, the facial expression editing model is used for performing facial expression editing on the facial image input by a user, and one or more edited images are provided; each edited image carries at least one expression in a plurality of preset different expressions; respectively inputting the facial image and each edited image into a facial feature extraction model to obtain a first facial feature corresponding to the facial image and a second facial feature corresponding to each edited image; and based on a first facial feature corresponding to the facial image and a second facial feature corresponding to each edited image, judging whether the facial image can be used for facial recognition processing.

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.

Disclosed herein are a facial recognition method and apparatus based on masking. The facial recognition method based on masking includes performing detection and normalization of a face area including five landmarks in an input image, separating a face portion other than a portion covered by an obstacle in the detected face area, generating a mask corresponding to the separated face portion when there is a portion covered by an obstacle in the separated face portion, applying the mask to an image in a face image registration database and extracting a feature of a masking region, and determining whether it is an identical person based on the extracted feature value.

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 embodiments execute machine-learning architectures for biometric-based identity recognition (e.g., speaker recognition, facial recognition) and deepfake detection (e.g., speaker deepfake detection, facial deepfake detection). The machine-learning architecture includes layers defining multiple scoring components, including sub-architectures for speaker deepfake detection, speaker recognition, facial deepfake detection, facial recognition, and lip-sync estimation engine. The machine-learning architecture extracts and analyzes various types of low-level features from both audio data and visual data, combines the various scores, and uses the scores to determine the likelihood that the audiovisual data contains deepfake content and the likelihood that a claimed identity of a person in the video matches to the identity of an expected or enrolled person. This enables the machine-learning architecture to perform identity recognition and verification, and deepfake detection, in an integrated fashion, for both audio data and visual data.