89 results about "Facial characteristic" patented technology

The application discloses an eye movement tracking method and system for the elderly based on eye-face double-flow timing interaction, and the method comprises the following steps: extracting a full-face region image and an eye local region image for an image frame of a face video sequence, extracting multi-scale face features for the full-face region image, performing layer-by-layer linear transformation, projection operation, splicing and fusion to construct a full-face token sequence; defining an eye prior dictionary based on the eye local region image, and encoding the eye local region image to obtain an eye feature representation, and further obtaining an enhanced eye feature representation; generating an eye query vector, obtaining a face key vector and a face value vector based on the full-face token sequence, and further obtaining an updated eye feature representation and a face feature representation optimized by gaze constraint; obtaining a differentially enhanced face feature representation; inputting the differentially enhanced face feature representation into a time sequence module to obtain a gaze point prediction result, and outputting an overall objective function.

Identify an object in various environments. 【Solution means】Communication terminal ( 2 ) is From the color image data using the first estimation model (30) 、 color image Colors that indicate the amount of facial features included a color feature estimation unit (40) that estimates a feature vector; From depth image data using the second estimation model (31) 、 depth image Depth of facial features included a depth feature estimation unit (41) that estimates a feature vector; From the color feature vector and depth feature vector using the third estimation model (32) 、 a three-dimensional image including a color image and a depth image Three-dimensional representation of facial features included in the image. a three-dimensional feature estimation unit (42) that estimates a feature vector; Color characteristics feature vector, depth degree characteristic feature vector, and three-dimensional Gentoku and an authentication unit (24) that performs face authentication using any of the feature vectors.

The present disclosure provides a system (102) and a method for preventing the unintentional operation of a sunroof in a vehicle. The system is configured to determine a multitude of facial attributes of an occupant in the vehicle using an imaging device. Furthermore, the system is configured to retrieve a profile of the occupant, corresponding to the determined multitude of facial attributes, from a profile database (218). In addition, the system is configured to calculate a occupant violation score based on the number of violation events committed by the occupant in relation to the operation of a sunroof unit (204). The calculated violation score corresponds to the occupant's profile. Furthermore, the system is configured to determine a violation level corresponding to the violation score.Furthermore, the system is configured to allow the locking of an occupant's seat belt based on the level of violation according to the violation point score.

This application provides a method, apparatus, device, and readable storage medium for determining a face mask. The method includes: recognizing a user's facial image to obtain facial features in the facial image; each facial feature represents the distance between two feature points in the facial image; when there is a reference object in the facial image, determining a target ratio between the projection size of the reference object in the facial image and the facial features, and determining the target ratio as a first type; the first type represents the size relationship between the projection size and the facial features; when there is no reference object in the facial image, determining a target ratio between two facial features, and determining the target ratio as a second type; the second type represents the size relationship between the two facial features; determining a mapping relationship corresponding to the ratio type of the target ratio, and determining the target face mask specification corresponding to the target ratio in the mapping relationship. This application can improve the efficiency of face mask determination.

The invention discloses a modular helmet for facial stimulation with temperature, pressure and vibration control that combines three forms of stimulation: temperature control, controlled pressure at specific points, and mechanical vibration. The modular helmet has an ergonomic modular configuration that ensures a complete and comfortable facial treatment experience, which can be fitted to different facial features, allowing the user to adjust the intensity and distribution of the stimuli based on their individual needs. The modular helmet has a control module comprising a microcontroller; a rechargeable battery that powers all electrical and electronic components; a wireless communication unit that allows wireless connection with a mobile device to configure its functions; and a screen and buttons that the user can use to set the vibration frequency, pressure intensity and the temperature level required.

Disclosed herein is an apparatus comprising a camera and a processing unit operatively coupled to the camera, wherein the processing unit is configured to: receive a sequence of images captured by the camera; process a first image of the received sequence of images to compute respective likelihoods of each of a plurality of predetermined facial features being visible in the first image; compute, from the computed likelihoods, a probability that the first image depicts a predetermined first side of a human head; responsive to at least the computed probability exceeding the predetermined detection probability, trigger performance of a predetermined action.

The present disclosure relates to a video generation method, device, medium, electronic equipment and program product. The method comprises: obtaining a first image, wherein the first image has object characteristics corresponding to a target object in a second image and has style characteristics corresponding to a target style; generating a first action animation according to the first image, wherein the first action animation is used to describe a basic dynamic effect of the target object; parsing semantics in a video script obtained and obtaining at least one action conforming to the semantics; superimposing the at least one action on the first action animation to obtain a second action animation; generating a lip shape action sequence according to obtained text and facial features of the target object in the first image; and fusing the lip shape action sequence, the second action animation and audio corresponding to the text in time sequence to generate a target video, thereby improving the quality of the generated video.

This invention discloses an image processing model training, processing method, system, device, and medium. The training method includes: acquiring original 3D facial data and several sample audio data; extracting sample facial features from the original 3D facial data; acquiring sample audio features from the sample audio data; adjusting the sample facial features based on the sample audio features to obtain corresponding sample 3D facial data; training a preset network using the sample facial features and sample audio features of each group as input and the corresponding sample 3D facial data as output to obtain an image processing model. By acquiring facial features through a graph convolutional neural network and obtaining audio features through multi-level processing, the image processing model obtained through convolutional neural network training exhibits higher accuracy, versatility, and robustness. Calculating the fusion loss of the fused features and the generation loss of the sample 3D facial data improves the efficiency and accuracy of model training.

A method for grouping facial feature objects in an image containing a number K of faces, which are respectively associated with a number K of people and each of which contains a number N of facial feature objects, includes: grouping a number (K*N) of entries of facial feature data into a number K of groups; calculating a match score that is associated with the entry of facial feature data and that indicates a probability of an entry of base facial feature data being correctly grouped for each of the number (K*N) of entries of facial feature data, which results in a number (K*N) of match scores; and implementing a re-grouping process with respect to an entry of target facial feature data that has a low match score, so as to obtain a number K of adjusted groups.

This disclosure relates to an expression-driving method, apparatus, device, and storage medium. The method includes: acquiring a face occlusion image; extracting facial features corresponding to unoccluded areas from the face occlusion image, and determining facial deformation parameters corresponding to the face occlusion image based on the facial features corresponding to the unoccluded areas; and driving the expression of a target virtual character based on the facial deformation parameters. Through this method, when the face is occluded, only the facial features corresponding to the unoccluded areas of the face occlusion image need to be focused on, and the facial deformation parameters for driving the expression of the target virtual character are obtained based on the facial features corresponding to the unoccluded areas. This achieves expression-driving for the target virtual character, avoiding the influence of occlusions in the face image on the facial feature analysis process. It ensures that the expression of the target virtual character can be effectively driven even when the face is occluded, guaranteeing the expression-driving effect of the target virtual character.

The application provides a smart mirror virtual makeup intelligent recommendation system, and relates to the technical field of data processing, and comprises an acquisition module configured to collect face image data of a user; based on the face image data, a plurality of feature regions of the face of the user are identified, including a left outer corner of the eye region, a right outer corner of the eye region and a lower nose tip region; center coordinates of the three feature regions are extracted, a face feature analysis circle frame is constructed, and based on the center, the radius and the distribution relationship of the three points relative to the center of the circle frame, structural characteristic analysis is performed on the face feature distribution in the frame to obtain face structure compensation parameters. The application realizes high matching of the virtual makeup effect with the actual scene and the skin state.

This application relates to a zero-interaction permission recognition method and system based on facial recognition and ERP / MES integration, belonging to the field of information security recognition technology. The recognition method includes: real-time acquisition of facial images through a multi-camera network deployed in different scenarios; preprocessing the facial images to construct a facial feature relationship network, extracting facial feature vectors, and performing liveness detection; outputting identity features when the liveness detection confidence exceeds a preset confidence threshold; comparing the identity features with a pre-stored employee feature database in the ERP / MES system in real time, and binding employee identity identifiers and real-time permission data based on the comparison results; automatically triggering corresponding device automatic authorization commands based on permission binding tags matching the current scenario; and recording the execution events of the device automatic authorization commands to generate structured audit logs. This application can achieve high-precision identity verification without the need for active user cooperation, and instantly trigger corresponding levels of device control and data access permissions accordingly.

The application discloses a football training posture analysis method and system based on image enhancement, and belongs to the field of image processing. The method comprises image acquisition, low-light training image processing, motion blur detail reconstruction, posture image transformation, training posture joint node estimation model design, and football training posture analysis. The scheme ensures the visibility of the player's jersey number and facial features in the shadow by local gain improvement and histogram equalization enhancement. Through motion blur detail reconstruction, the blurred texture and sharp outline are restored. The design of the fidelity loss includes a modulus deviation term and a structure correlation error term, which makes the predicted joint heat map more accurate in the direction of the toe and the degree of finger opening. Through phased timing fine adjustment, the details of early frames are avoided from being smoothed, and the details of the football training posture are protected. The analysis effect of the football training posture is improved.

This invention relates to the field of health data analysis technology, and particularly to a method and application for analyzing the correlation between eating micro-expressions and food satisfaction based on deep learning. The method includes the following steps: S1, capturing facial video images of users during the eating process using a camera device, and extracting eating facial feature sequences from them; S2, separating chewing actions and facial muscle movements from the eating facial feature sequences based on the spatial displacement differences of facial key points, and obtaining target micro-expression feature vectors. In this invention, by cross-evaluating the captured eating micro-expression emotional state with the user's physiological health indicators, nutritional threshold constraints and weight corrections are applied to the initial food preferences generated based on emotions. This allows for strict control of the intake of core risk nutrients while catering to the user's personal taste satisfaction, ultimately generating personalized recommended recipes that balance emotional experience and medical health standards.

This invention discloses a robot interaction method, device, electronic device, and storage medium. The method includes: detecting first pose parameters of the first facial target in response to a first condition being triggered by a first facial target; detecting first feature information of the first facial target in response to a angular difference between the facial orientation indicated by the first pose parameters and the optical axis of the target robot's camera being less than a preset angular difference, wherein the first feature information is used to indicate facial feature information and gesture feature information; determining the task intent associated with the first gesture feature information from multiple sets of task intents associated with the first gesture feature information based on the first gesture feature information in response to a matching degree greater than a preset matching degree of the first feature information and a third feature information among multiple sets of second feature information; and controlling the target robot to execute a corresponding feedback action based on the task intent associated with the first gesture feature information. This invention can improve the environmental anti-interference capability and intent recognition accuracy of robot interaction.

This invention discloses a multimodal fusion-based intelligent monitoring method for cat health and emotions. The method specifically includes: cleaning and extracting features from the original multimodal data to obtain acoustic features, facial features, fur features, and body temperature features; normalizing the acoustic, facial, fur, and body temperature features, and performing dimensionality reduction using principal component analysis and t-distribution random neighborhood embedding to extract a cross-modal core fusion feature set; and using a dynamically weighted random forest classifier to make decisions based on the cross-modal core fusion feature set, identifying the cat's health and emotional state, and outputting the monitoring results. This invention integrates four types of data—cat voice, facial expression, fur condition, and body temperature—and achieves accurate and real-time monitoring of the cat's physiological health and emotional state through a two-level fusion algorithm design involving feature layer and decision layer.

1. Name of the product in this design: Dynamic Face Recognition Access Control Machine (Starway 8). 2. Application of this design: To identify individuals using facial features for purposes such as attendance management, access control, payment processing, and visitor management. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

The application discloses a three-dimensional face reconstruction method, device and equipment based on facial electromyographic signals, comprising the following steps: preprocessing original signals including facial electromyographic signals and video frame data collected to obtain electromyographic signal sequences and video frame sequences; based on the video frame sequences, facial key points are extracted and identity information is removed to obtain standardized facial feature point sequences; a pre-network including a convolutional neural network module and a Transformer encoder module is self-supervised pre-trained using a facial electromyographic signal dataset to obtain a facial feature extraction model; the facial feature extraction model is used as a fixed-parameter feature extractor and is connected with a Transformer decoder module to train a cross-modal mapping model from the electromyographic signal sequences to the standardized facial feature point sequences; the facial electromyographic signals to be reconstructed are input into the cross-modal mapping model to obtain facial feature point sequences, and the facial feature point sequences are input into a preset statistical three-dimensional head model to generate corresponding three-dimensional face reconstruction results.

The application relates to a face recognition processing method and device, computer equipment and a storage medium. The method comprises the following steps: taking the collection angle of the first face information of a target user as a first angle, and extracting the first angle face feature of the target user from the first face information; performing similarity evaluation on the first angle face feature and a plurality of reference face features in a face feature library to obtain a first angle score of the target user; when the first angle score is lower than a score threshold, collecting second face information of the target user based on a second angle different from the first angle to obtain a second angle face feature; performing weighted fusion on the first angle score and a second angle score obtained based on the second angle face feature to obtain a comprehensive score of the target user; and when the comprehensive score is not lower than the score threshold, determining that the target user meets a face recognition condition and performing face recognition processing on the target user. The method can improve the face recognition success rate.

We provide the system. [Solution] A device that acquires the user's facial image, A device that analyzes acquired facial images and extracts facial features, A device that compares extracted features with past data to propose a suitable beauty method, A device that selects consumer goods related to the proposed beauty method and provides a means of purchasing them, A system that includes means for customers to try out consumer goods offered within a store.

A standalone digital display device that includes a visual sensor to capture images of a user's face and video scaler hardware controller executing a facial detection module to detect facial patterns of a user's face detected in front of the standalone digital display device in the captured images. The video scaler hardware controller to access the scaler storage device on the standalone display to determine if the detected facial patterns of the user's face match a facial pattern stored on the scaler storage device among a plurality of stored facial patterns. The video scaler hardware controller executes computer-readable program code of a profile bridging firmware module to, when a matching facial pattern is found, automatically apply user-selected on-screen display settings associated with the matched facial pattern independently from an operatively coupled information handling system to video data received from the operatively coupled information handling system for display of images.

Various embodiments of the present disclosure provide a gesture translation pipeline that improves the functionality of a computer in various aspects. The techniques comprise receiving an image that depicts a facial expression and a hand position of a user, generating, using a parallel feature extraction model of a multi-stage machine learning architecture, a set of facial features and a set of hand features from the image, generating, using an aggregation model of the multi-stage machine learning architecture, a text prediction corresponding to the image based on the set of facial features, the set of hand features, and a set of defined terms associated with the multi-stage machine learning architecture, and initiating a prediction-based action based on the text prediction.

The cattle facial recognition method and system belong to the field of biometrics. It solves the problems of existing cattle recognition methods, such as high false recognition risk, low recognition efficiency, high cost, and specific requirements for recognition equipment and environment, making them unsuitable for modern large-scale farms. The method includes the following steps: Step S1, acquiring images and preprocessing them to establish an image dataset, which includes a facial feature dataset and its corresponding identity information dataset; Step S2, performing target detection on the facial feature dataset based on the YOLOv7 model; Step S3, recognizing the detected facial feature dataset using the FaceNet facial recognition method, thus completing the recognition of the facial feature dataset and simultaneously displaying the corresponding identity information dataset.

This application discloses a face recognition method, apparatus, storage medium, and electronic device. The method includes: calculating the similarity between the facial features of a person to be identified and the facial features of candidate comparison persons to obtain a candidate comparison similarity; selecting candidate comparison persons from a pre-stored database; determining a candidate dynamic comparison threshold condition pre-associated with the candidate comparison persons; wherein the dynamic comparison threshold condition is determined based on the facial identifier of the comparison persons, the comparison similarity, and scene information; if the candidate comparison similarity meets the candidate dynamic comparison threshold condition, then the face recognition between the person to be identified and the candidate comparison persons is successful. This technical solution, based on an adaptive dynamic comparison threshold for face recognition, can effectively improve the scene adaptability and ease of use of face recognition.

The application discloses a kind of acne detection systems, system includes: acquisition module, for acquiring face image, and the interactive data corresponding to face image is collected, wherein, interactive data includes the position frame corresponding to acne in face image and symptom label;Feature extraction module is used to carry out feature extraction to face image, and obtain face feature vector;Multi-modal fusion module is used to carry out vector splicing processing to face feature vector, position frame and symptom label, and obtain joint feature vector;Classification module is used to classify joint feature vector, and obtain detection result.The reliability of auxiliary diagnosis can be improved by the embodiment of the application.

The application discloses a role animation generation method and device, electronic equipment and a storage medium, comprising: obtaining identity indication information of a target role, the identity indication information comprising at least one role identity feature of the target role, and one role identity feature indicating one visual performance effect of the target role; obtaining a role expression reference image matched between the target role and the identity indication information; outputting at least one image frame of the target role based on the identity indication information, a first prompt word and a second prompt word through a target model; and generating a role animation of the target role based on each image frame; the application can accurately maintain the unity of the facial features of the role and the stability of the appearance features of the role in the continuous image frame sequence generation process of the animation, thereby improving the role consistency of the role in the animation and effectively improving the generation efficiency of the animation.

Provided is a method for encoding a video, an electronic device and a storage medium, relating to the field of video encoding. The method includes: obtaining a reference frame that is adjacent to a current frame and has established hierarchical division of an initial ROI; reusing a motion vector generated by the reference frame in a motion compensated temporal filter process to determine an ROI position prediction value of the current frame; extracting facial feature points from the current frame, and correcting the ROI position prediction value based on a spatial topological relationship of feature points in the initial ROI; configuring differentiated quantization parameters for a main face region, a secondary face region and a background region after correction respectively; dynamically allocating a three-region bitrate according to a real-time network bandwidth; and outputting an encoded frame of the current frame and associated ROI-level metadata.

The application relates to the technical field of artificial intelligence, and relates to a child social accompanying robot interaction system based on emotion recognition, which comprises a multi-source perception and feature extraction module, a cross-modal correlation analysis module, a multi-dimensional emotion fusion module, an emotion strategy mapping and generation module, a double-channel instruction compiling module and a collaborative execution and interaction response module. Multi-scale feature extraction is performed on a face image and a voice signal to obtain a face feature vector and a voice emotion vector. A correlation weight matrix of a target child is constructed. The face feature vector and the voice feature vector are coupled in multiple dimensions to obtain a composite emotion feature. The emotion state is mapped in a strategy mode to obtain an emotion interaction strategy. The composite emotion feature is compiled in an instruction mode to obtain a voice content instruction and an expression action instruction. The voice content instruction and the expression action instruction are input into an execution terminal to obtain an emotion accompanying interaction response of the robot. The application can improve the efficiency of child emotion interaction response.