277 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.

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.

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.

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.

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.

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 invention provides a wounded consciousness state evaluation system based on face recognition, and relates to the technical field of health evaluation. Comprising a facial information extraction module, a facial feature analysis module, a consciousness state calculation module, an anomaly detection module and a consciousness state evaluation module. According to the invention, the infrared camera carefully captures the fine movement of the muscles around the eyes, the mouth corner and the frontal part, the accuracy and comprehensiveness of data acquisition are improved, the accuracy and dimension of evaluation are improved by evaluating the blinking frequency, the mouth corner displacement and the frontal muscle stretching amplitude, and by utilizing the extraction of the deformation characteristics and the calculation of the movement amplitude gradient, the accuracy and the comprehensiveness of the evaluation are improved. The time sequence analysis is combined, the evaluation of the consciousness state becomes more sensitive and accurate, potential changes of consciousness can be found in time, and therefore, rich data interpretation is provided for diagnosis and monitoring through quick response, and the density and distribution characteristic analysis of sudden change points, so that the consciousness state can be quickly and accurately evaluated under the condition that a wounded person is not contacted.

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.

The invention discloses a self-adaptive fatigue state detection method based on multi-modal feature fusion, and belongs to the technical field of fatigue detection, and the method comprises the steps: synchronously collecting facial images through an infrared camera and an RGB camera, carrying out the image fusion through a Laplacian pyramid method, extracting facial features, setting a personalized threshold value, and obtaining fatigue features. Performing special scene judgment under limited face recognition, considering micro-expression features, performing fatigue parameter supplementation, judging a fatigue state through a comprehensive fatigue model with multi-fatigue feature fusion, and performing early warning information reminding; according to the adaptive fatigue state detection method based on multi-modal feature fusion, through multi-modal feature fusion, image details are effectively reserved, a low-light environment is adapted, facial feature deficiency is compensated, and the accuracy and stability of fatigue state detection are remarkably improved by combining personalized threshold values and special scene judgment.

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.