484 results about "Facial region" patented technology

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.

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 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 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.

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.

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.

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 system and method for real-time facial and sentiment detection using a computing system. The system includes a video input module that receives real-time video input from various sources such as webcams, security cameras, and smartphone cameras. The video frames are pre-processed by adjusting the resolution, converting color spaces, and isolating the foreground from the background. A facial detection module employs a convolutional neural network to identify and localize human facial regions within the video frames. Geometric and appearance features are extracted from the localized facial regions by a feature extraction module. A sentiment classification module classifies the extracted features to determine sentiments using a deep learning model. The system also includes a module for API integration, enabling third-party applications to utilize the sentiment recognition results.

A system and method for automatically adjusting lighting settings to optimize the image quality of a video feed is disclosed. The method includes receiving a video feed from a camera, detecting a face within the video feed, and defining a facial region bounding the detected face. The method also includes analyzing the facial region and determining an image characteristic corresponding to the facial region. The method can also include sending commands to a lighting device to automatically adjust one or more lighting settings based on the value of the image characteristic. Such a system may be used in a variety of applications, including video calls.

The invention discloses a fatigue driving detection method based on personalized feature fusion, and the method comprises the following steps: facial feature extraction and personalized dynamic adaptation: employing a facial detection algorithm to extract facial features of a driver; aiming at the facial features of different drivers, automatically adjusting the feature weight of each facial region through a personalized dynamic adaptation mechanism; performing spatial-temporal feature fusion and fatigue behavior pattern recognition: performing fusion through a multi-dimensional time sequence convolutional neural network to obtain a comprehensive fatigue recognition value; performing weighted fusion on the facial features, the driving behavior features and the vehicle state features to obtain final fatigue recognition output; and fatigue recognition output: integrating all the fused features and the optimized model, and outputting a final fatigue driving recognition result. According to the method, the facial feature weight is dynamically adjusted, spatial-temporal features and multi-source data fusion are combined, the adaptability and precision of the system can be improved, and it is ensured that the fatigue state is accurately detected under different drivers and driving environments.

An information processing apparatus performs face detection processing to detect a face area with a face captured therein from an image captured by an imaging unit, brightness reduction processing in which, when the face area is no longer detected from a state where the face area is detected by the face detection processing 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.

The invention relates to the technical field of model construction, and provides an industrial manufacturing method based on a digital human basic model and related equipment thereof. The method comprises the following steps: performing target person facial region extraction on an input image to obtain a target region image, performing gender recognition on a target person to determine a basic model type, and performing facial feature point extraction on the target region image by using a coordinate recognition model to obtain a facial feature point coordinate set; then, the extracted facial feature point coordinates are mapped to the basic model in a feature matching mode to generate a first three-dimensional model, and in order to improve the details of the three-dimensional model, target detail enhancement is carried out on the first three-dimensional model according to a preset geometrical shape adjustment strategy to obtain a second three-dimensional model; and finally, performing structure optimization on the second three-dimensional model according to a preset model optimization strategy to generate a third three-dimensional model. According to the method, facial feature extraction and matching and model optimization are automatically carried out, so that the production efficiency and quality of the digital human are improved.

The invention provides a display device and a figure skin color compensation method, and the method comprises the steps: recognizing a partition boundary through a boundary pixel difference value, dividing a face into a preset number of partitions, and screening a to-be-compensated region based on the deviation between each partition and an original skin color pixel value. According to the method, a local area with abnormal chromatic aberration caused by the illumination condition in the video frame can be accurately positioned, unnecessary adjustment of a normal skin color area is avoided, and more targeted compensation operation can be executed on a target person. According to the method, the original skin color pixel value of a normal skin color area is reserved, targeted skin color compensation is executed by dividing a plurality of face subareas, and the skin color secondary distortion phenomenon caused by executing skin color compensation on the whole face area is effectively eliminated.

The invention discloses a personalized 3D printing mask based on MRI (Magnetic Resonance Imaging) reconstruction and a preparation method thereof. The method comprises the following steps: scanning a human face by using magnetic resonance equipment to obtain human face MRI data, and generating a human face three-dimensional model according to the human face MRI data; taking the human face three-dimensional model as a reference to generate a three-dimensional mold which is completely fitted with the face contour; extracting physiological parameters of the human face skin from the human face MRI data by adopting a medical image platform, and performing quantitative analysis on the physiological state of the human face skin according to the physiological parameters to obtain a quantitative analysis result; and adopting biological 3D printing equipment, taking a three-dimensional mold as a carrier, and combining the quantitative analysis result to carry out 3D printing, so as to obtain the 3D printing mask. The 3D printing facial mask prepared by the method can be highly fit with the facial contour of a human body, and skin problems of different facial areas can be accurately nursed.

The invention provides a real-time physiological parameter non-inductive monitoring method based on visual perception and intelligent calculation, belongs to the technical field of machine vision and deep learning, and aims at solving the problems that a traditional physiological parameter monitoring method is uncomfortable and high in cost. Video image acquisition equipment such as a common camera is used for acquiring video images of a face area of a subject, and the heart rate value, the heart rate variability, the oxyhemoglobin saturation and the blood pressure value of the subject are obtained; and establishing a fatigue degree evaluation network for the obtained physiological index parameters, and comprehensively evaluating the fatigue degree of the testee. The system has the advantages of good comfort, high precision and automatic monitoring function, can accurately monitor various physiological parameters such as heart rate, heart rate variability, blood oxygen saturation, blood pressure and fatigue degree of a subject, ensures the real-time performance of the monitoring process, and meets the application requirements of multiple fields.

Disclosed are a method and a system for mental state perception and a computer-readable storage medium. The method includes: acquiring image sequences with timestamps and millimeter-wave radar raw data with timestamps; preprocessing the image sequences and the millimeter-wave radar raw data; analyzing the head region image sequences to obtain head vibration signal features; calculating face region image sequences obtained from preprocessing by using a remote photovolumetric pulse wave recording method to obtain a first heart rate; analyzing the original millimeter-wave radar data sequence to obtain a second heart rate and a breathing rate; fusing the first heart rate, the second heart rate and the breathing rate to obtain a fused heart rate and breathing rate; performing feature extraction on facial change information by a Transformer-like network; establishing a non-contact multi-modal mental perception model for prediction to obtain a predicted result of the mental state.

A method for measuring a non-contact biometric information, includes steps of photographing an image including a user's face; detecting a face area in a frame of a photographed image; detecting a landmark within the face area and starting a tracking mode when a specific landmark is detected; detecting a movement of the face area in the tracking mode and determining a section in which the movement of the face is less than a reference value; and extracting a skin area within the face area in the determined section and acquiring pixel change component data of the skin area.

A phototherapy face mask is provided for emitting therapeutic radiation towards the facial region of a user and aims to restore the youthful appearance of the user who is wearing the mask. The phototherapy mask comprises an outer surface and an inner surface, wherein the inner surface is aligned with the facial skin of the user. A flexible PCB layer is provided between the inner surface and the outer surface, on which a plurality of coherent light sources such as LEDs are mounted. The light sources are configured to emit radiation preferably in the visible and infrared range of the electromagnetic spectrum. The mask further includes a medicament chamber which is operably coupled to a piezoelectric pump that dispenses the medicament at regular intervals. The medicament is uniformly distributed all over the facial region of the user through a plurality of pores of the mask.

A mannequin model video real-person clothing display system based on a diffusion model comprises a data acquisition unit used for acquiring a mannequin model video and a target identity image; and the video generation unit is used for converting the mannequin model video into a real person video with a target identity feature. The video generation unit comprises a feature encoder used for encoding a high-dimensional image and a video into a low-dimensional hidden space; the dynamic posture sensing head encoder is used for fusing the facial appearance features of the target identity image, the facial key point postures of the target identity image and the human body posture sequence features corresponding to the mannequin model video so as to generate posture sensing identity embedding features; the distribution perception adapter is used for carrying out distribution alignment on the identity embedding characteristics and the clothing characteristics; the diffusion video generation network based on the hidden space is used for de-noising reasoning; and the mirror image loss calculation module is used for generating a face region of the video and calculating pixel-level reconstruction loss and perception loss.