599 results about "Face detection" patented technology

The invention provides a face recognition method and device, electronic equipment and a medium, and the method comprises the steps: processing a scaled to-be-recognized face image through a face detection model, and determining the coordinates of a face region and a plurality of key points in the original to-be-recognized face image based on the recognition result of the face detection model; based on the coordinates of the plurality of key points in the to-be-recognized face image and the coordinates of the plurality of key points in a preset face template, aligning a face region in the to-be-recognized face image to the preset face template, and outputting an aligned face image of a preset size; processing the aligned face image through a face recognition model comprising a down-sampling module, and generating a target face feature of the face image to be recognized; the user information of the target user corresponding to the to-be-recognized face image is determined based on the similarity between the target face feature of the to-be-recognized face image and the feature vector of the user in the database, so that the calculation amount in the face recognition process is reduced, and the accuracy and precision are both considered.

A method of maintaining the security of an authentication session comprises operating a camera device to continuously capture a view of an environment of a computing device, performing face detection to detect all faces in the environment, performing comparisons between faces detected in the environment and faces of a set of authorized users, at a first time, detecting a first face of an authorized user of the set in the view of the environment and, in response, allowing the authorized user to operate the computing device, and at a second time after the first time, one of failing to detect the first face or detecting a second face that is not of an authorized user of the set in the view of the environment, and, in response, at least partially blocking operator access to input and output devices including blocking a display of the computing device.

SYSTEMS AND METHODS IN THE FIELD OF SELF-SUPERVISED DETECTION OF FACIAL BOUNDARIES. Systems and methods for self-supervised learning (SSL) in facial detection networks are proposed. In one embodiment, a facial detection network comprises encoder components configured to encode facial features, the encoder components including trained components of a masked image modeling (MIM) network configured to process non-overlapping patches determined from the input image, the MIM network trained with an SSL objective; and decoder components configured by training to determine local matches between features to determine estimates for facial landmarks. In one embodiment, the MIM network is an MAE network.In one embodiment, the decoder components are derived from those of a second trained network comprising the encoder components as trained but fixed, wherein the decoder components of the second network are trained using locality constraint repulsion loss (LCR). Methods are proposed for SSL training of the encoder and decoder components. Figure for abstract: none.

The invention relates to the technical field of face recognition, and discloses a face recognition system and a detection method thereof, and the recognition system is composed of a face image acquisition module, an environment perception and preprocessing module, a face detection feature extraction module, a comprehensive analysis and comparison module and a data storage monitoring module. According to the method, environmental factors such as illumination intensity, light source type and color temperature are detected through a sensor, corresponding preprocessing is carried out on the image, the influence of the environmental factors on the recognition precision can be reduced by correcting illumination unevenness, denoising and enhancing contrast and sharpness, meanwhile, image compensation and denoising are carried out, illumination unevenness is compensated, and denoising processing is carried out, so that the recognition precision is improved. The beneficial effects of comprehensive analysis of age, health conditions and illumination condition environmental factors through face recognition and more stable recognition precision are achieved.

The invention discloses a campus intelligent alarm linkage method and system based on face recognition, and the method comprises the steps: collecting a video stream in real time, and extracting a face image in a video frame through a face detection algorithm; comparing the face image with a white list database pre-stored in a campus in real time, and generating an identity recognition result and a confidence score of the face; calculating the threat level of the current behavior scene through a dynamic early warning judgment model and generating a graded early warning signal based on the identity recognition result and the confidence score in combination with the real-time analysis of the personnel behavior scene; automatically triggering a corresponding equipment linkage instruction according to the graded early warning signal; and on the basis of the execution state of the equipment linkage instruction and a subsequent video analysis result, generating a complete security event disposal report containing an incident position, an incident-related personnel image and a disposal suggestion. According to the embodiment of the invention, the active early warning capability and the emergency response efficiency of the campus security system can be improved.

The invention relates to an image detection method based on multi-contrast learning, belongs to the technical field of image processing and deep learning, solves the problem that a forgery detection method in the prior art is poor in generalization ability when processing unknown forgery types, and comprises the following steps: S1, establishing a detection model comprising a feature extraction module, performing data preprocessing and feature extraction on the input image to obtain a feature map; s2, establishing a comparative learning module, and pre-training the detection model to obtain a pre-trained detection model; s3, establishing a distillation module which comprises a teacher model and a student model, and transferring knowledge of the teacher model to the student model; s4, establishing a multi-loss joint optimization module, and performing joint optimization in combination with the classification loss, the comparison loss and the distillation loss to obtain an optimized student model; s5, establishing an output prediction module, and generating and outputting a detection result; and S6, inputting the to-be-detected image into the image detection model to obtain a face detection result.

The invention discloses a hairline detection method based on multi-scale feature fusion, and relates to the technical field of hairline detection. Comprising the steps of 1, building a multi-angle face image acquisition platform comprising an image acquisition module, a face detection module and a quality evaluation module, and obtaining high-quality face image data comprising a hairline area; step 2, preprocessing the acquired face image data and enhancing hairline sensing data; and step 3, constructing a hairline detection network model based on Hairline SegNet, wherein the network model comprises an improved ConvNext-B encoder which comprises four feature extraction stages and is used as a backbone network. According to the method, the Hairline SegNet special network for hairline detection task optimization is arranged, the hairline perception enhancement module and the multi-scale feature fusion module are integrated, accurate capture of hairline fine-grained features is achieved, and the problems that a general segmentation network lacks special optimization, and global and local details cannot be considered in single-scale feature extraction are solved.

The invention provides an electrical stimulation task processing method and device and terminal equipment, and the method comprises the steps: inputting a collected user face image into a pre-trained face detection model for face recognition, and obtaining a face region recognition result; inputting the face region recognition result into a pre-trained key point recognition model for key point detection to obtain a face key point recognition result; constructing a face feature coordinate system based on the face key point recognition result, and mapping the face acupoints to the face feature coordinate system in combination with a preset mapping rule to obtain a user face acupoint positioning result; performing acupoint impedance detection according to a user face acupoint positioning result, calling a corresponding electrical stimulation waveform according to an electrical stimulation task initiated by the user when the acupoint impedance detection result is passed, and executing the electrical stimulation task based on the electrical stimulation waveform. According to the invention, the alignment accuracy and the treatment effectiveness of the TENS equipment in the use process can be improved.

The invention discloses a door lock video recording method and system based on event value driving, and the method comprises the steps: detecting an event of a person according to a first door lock, obtaining a video stream address of a second door lock through door relation information, and synchronously caching video streams of the door locks of both parties; judging event types according to video analysis results, storing complete video streams of both parties for important events, and only storing panoramic images for non-important events; according to the door lock face detection time difference and the residual storage space of the two parties, negotiating to determine a single door lock to store video streams of the two parties; and according to a user query request, locally and directly responding or obtaining video data through door collaboration to finish query display. By utilizing the embodiment of the invention, efficient video acquisition and storage can be realized through cooperation of event driving and a door lock, the integrity of security and protection video records is improved, the storage space is saved, and more efficient and reliable intelligent query response is supported.

Implementations described herein relate to a method, system, and computer-readable medium for generating animation for a 3D avatar from input video and audio captured on a client device. A camera may capture facial video, and a trained face detection model and a trained regression model output a set of video FACS weights, head pose, and facial landmarks that will be converted into animation for the 3D avatar. In addition, a microphone may capture audio emitted by a user, and a trained face motion detection model and a trained regression model output a set of audio FACS weights. In addition, a blending term is provided for identifying time progression in the audio. A modular blending component blends the video FACS weights and audio FACS weights based on the blending term to create final FACS weights for animating the user's avatar, a character's clothing, or another animation-enabled construct.

The invention belongs to the technical field of semiconductor material detection, and provides a single crystal silicon rod defect detection method which comprises the following steps: controlling a clamping and overturning module to clamp and fix two end surfaces of a single crystal silicon rod; controlling the end face detection module to collect and analyze at least one end face image, and outputting a first detection result about whether the end face is defective; controlling the single crystal silicon rod to continuously rotate around a central shaft, synchronously scanning and shooting the first half side surface, and obtaining a first half side surface panoramic image; the clamping and overturning module is controlled to loosen and overturn the single crystal silicon rod by 180 degrees and then clamp the single crystal silicon rod again, the reference position of the side surface is detected through a laser displacement sensor, and the circumferential angle is finely adjusted based on the detection result to complete pose calibration; s3 is repeated to obtain a second half-side panoramic image connected with the first half-side panoramic image; and splicing the two panoramic images based on the pose calibration result, synthesizing and analyzing a side panoramic image, and outputting a second detection result about whether the side is defective or not. According to the invention, the automation degree and the detection precision of single crystal silicon rod defect detection are improved.

The invention discloses a workpiece surface all-dimensional intelligent defect detection system and method in the technical field of workpiece detection, and aims to solve the technical problems of single illumination direction, limited shooting visual angle, unstable image quality, detection result feedback lagging and data management dispersion of existing detection equipment. The system comprises a detection table module, a synchronous belt sliding table vision module, a workpiece side face detection module, a side face light source, a six-axis mechanical arm, an annular light source, a control computer, a three-color lamp, a main rack, a baffle and a control button. Automatic workpiece positioning, uniform illumination, full-surface imaging, real-time defect recognition and standardized data archiving are achieved through cooperation of the multiple modules. The detection coverage rate is larger than or equal to 99%, the defect recognition accuracy rate is larger than or equal to 98%, the single-workpiece detection period is smaller than or equal to 30 seconds, the method is suitable for surface defect detection of workpieces such as metal and castings, and the high-beat and high-precision detection requirements of the modern manufacturing industry are met.

The invention provides a face data processing method and device, electronic equipment and a storage medium, and relates to the technical field of face data. The method comprises the following steps: performing spatial configuration based on original coordinates of a plurality of key points obtained from an original 3D image, and constructing an original configuration and a mirror image configuration corresponding to the original configuration; obtaining a median sagittal plane corresponding to the original 3D image based on a target distance between the original configuration and the mirror image configuration; and correcting the original 3D image based on the median sagittal plane to obtain the corrected original 3D image so as to perform face detection on the face of the user. Based on the scheme, the construction accuracy of the median sagittal plane can be improved, so that a doctor can diagnose the facial deformity through the median sagittal plane with higher accuracy, the accuracy of the doctor in diagnosing the facial deformity can be improved, and the doctor can favorably treat the facial deformity and evaluate the postoperative treatment effect.

The invention discloses an automatic guide ring detection device and method, and relates to the technical field of guide ring detection, the automatic guide ring detection device comprises a lower rack, the lower rack is provided with an upper rack, and the lower rack is provided with a bottom plate; the carrying mechanisms are arranged on the bottom plate and used for carrying the guide rings; the feeding conveying assembly is used for conveying the guide rings to the carrying mechanism; the external dimension detection mechanism is arranged on the bottom plate; the go gauge detection mechanism is close to the external dimension detection mechanism; the discharging assembly is arranged on the bottom plate; and the end face detection mechanism is arranged on the bottom plate. Through cooperation of the vibration feeding assembly, the feeding conveying assembly, the three carrying mechanisms, the detection mechanisms, the discharging mechanism and the discharging mechanism, full-process automatic operation of feeding, carrying, multi-project detection, sorting and discharging of the guide rings is achieved, manual intervention is not needed, the detection efficiency is effectively improved, and the manual labor intensity and manual detection errors are reduced.

The invention belongs to the technical field of image processing and privacy protection, and particularly relates to a deep face anonymization standardization processing and evaluation system. The system comprises a data preprocessing module, a multi-technology integration anonymization module, a cross-scene test set module, a multi-dimensional evaluation module and a visual interaction module. The data preprocessing module realizes face data standardization processing by unifying image size, resolution and color depth standards and combining Dlib face detection and feature point positioning; the multi-technology integration anonymization module packages mainstream anonymization technologies such as FIT, G2Face, DeepPrivacy and the like, and provides an extensible interface; the cross-scene test set module covers face samples of different illumination, postures and ages; the multi-dimensional evaluation module constructs an evaluation system from privacy protection intensity (re-recognition rate, cosine similarity and the like), image quality (FID, SSIM and the like), attribute maintenance (difference measurement of expressions, hairstyles and the like) and processing efficiency (time / memory consumption); and the visual interaction module supports data uploading, technology selection, result viewing and automatic report generation. According to the method, the problems of no unified architecture and evaluation fragmentation in the prior art are solved, standardized processing and fair evaluation of a face anonymization technology are realized, and a privacy protection technology is promoted to be applied to the ground.

The present invention provides a method of masked face recognition from images by artificial intelligence technology including four steps: step 1: generating the images of face wearing a mask; step 2: training a deep learning model for face detection while wearing a mask; step 3: training the deep learning model for face feature extraction while wearing a mask; step 4: building a full pipeline of masked face recognition from images using the trained models from step 2, step 3, and some post-processing algorithms. The method aims to improve the accuracy of identity verification in the context of wearing masks has become popular and compulsory in life.

The utility model discloses a packaging box six-face detection device which comprises an equipment frame, a feeding mechanism and a discharging mechanism are symmetrically arranged at the two ends of the equipment frame, a conveying mechanism is arranged between the feeding mechanism and the discharging mechanism, a detection mechanism is arranged at the upper end of the conveying mechanism, and the feeding mechanism and the discharging mechanism are the same in structure and are arranged at the two ends of the equipment frame in a mirror image mode. Products are conveyed on the conveying mechanism. The device has the beneficial effects that the defect detection precision can reach 0.2 mm * 0.2 mm, the minimum size of detectable foreign matters is 0.5 * 0.5 mm, the device can be compatible with 8-14-inch products, the maximum size of detectable color boxes is 380 * 280 * 70mm, the productivity can be improved to 250Pcs / H, the device can be independently used offline and can also be used in series, the left packaging boxes automatically flow in, online detection is realized by equipment through a visual grabbing feature comparison template, and the detection efficiency is greatly improved. And the efficiency and the product yield are greatly improved.

Systems and methods for face annotation are described. One or more of the systems and methods include receiving a plurality of annotated images, wherein each annotated image of the annotated images comprises a caption; cropping the annotated image based on a face detection algorithm to obtain a face crop; comparing the face crop to the caption corresponding to the annotated image to obtain a caption similarity score; and filtering the plurality of annotated images based on the caption similarity score to obtain a plurality of annotated face images.

The invention discloses a fatigue detection method and system based on a time sequence reconvolution network and electronic equipment, and relates to the field of fatigue detection, and the method comprises the steps: A1, collecting a face image of a driver in a vehicle driving process, carrying out the framing processing of the face image, and generating a data set; a2, constructing a time sequence reconvolution network model; step A3, taking a peak signal-to-noise ratio (PSNR) as a loss function, and training the time sequence reconvolution network model by using the training data set to obtain a trained model; step A4, inputting the framed face image into the trained model, and performing image enhancement and de-noising processing; step A5, adopting an MTCNN multi-task cascade convolutional neural network to carry out face detection on the enhanced and denoised image, and positioning two-eye key points and mouth key points; step A6, calculating an eye aspect ratio EAR and a mouth aspect ratio MAR; and A7, outputting the fatigue state of the driver according to the values of the EAR and the MAR and a preset fatigue state judgment rule.

The application relates to a face direction correction method and device, computer equipment and a storage medium. The method comprises the following steps: obtaining a face image to be detected, performing face detection on the face image to be detected, obtaining at least one group of face positioning points and at least one face candidate frame corresponding to each group of face positioning points; determining a confidence degree according to positioning accuracy and a classification probability value; screening a target detection frame corresponding to each group of face positioning points from the face candidate frame based on the confidence degree of each face candidate frame; obtaining face marking points; the face marking points are face positioning points in a face image in a standard posture; determining a direction correction matrix corresponding to each group of face positioning points according to the face marking points and the face positioning points; and performing face direction correction based on the target detection frame corresponding to each group of face positioning points and the direction correction matrix corresponding to the face positioning points. The face direction can be corrected by using the method.

The invention discloses a speech emotion recognition method based on multi-modal multi-view pseudo-tag fusion, and relates to the technical field of emotion recognition, and the method comprises the steps: firstly, constructing a visual-view pseudo-tag generation module, extracting high-quality facial expression features from a video through employing an optimized OfficientNet-V2 network and a RetinaFace face detector, and carrying out the recognition of the high-quality facial expression features; generating a visual pseudo label through time sequence aggregation; secondly, adopting a semi-supervised learning framework FixMatch to generate a reliable voice pseudo tag for the non-tag voice; secondly, fusing emotional knowledge from multiple visual angles and modes of vision and voice based on a pseudo-tag fusion function, and generating a more accurate and robust fused pseudo-tag; and finally, training a voice emotion recognition model based on a Conformer encoder to perform voice emotion recognition by combining the fused pseudo label data with the labeled voice data. According to the method, the problem of error accumulation in traditional semi-supervised learning is effectively solved, cross-modal emotion complementarity is utilized, and the accuracy and generalization ability of speech emotion recognition are remarkably improved.

The utility model discloses a gasket reinspection device with an automatic detection function, and relates to the field of gasket detection devices. The gasket detection device comprises a supporting frame and a rotary table, a bottom face detection mechanism, a top face detection mechanism, a thickness detection mechanism and a removing mechanism are arranged on the outer side of the rotary table, a magnetic attraction mechanism is arranged below the rotary table, and in order to enhance the stability of gaskets on the rotary table, the magnetic attraction mechanism is particularly arranged below the rotary table. Power-on and power-off of the electromagnet are accurately controlled through the controller, so that the electromagnet can generate magnetic force to adsorb the gasket, in the process that the turntable rotates and transports the gasket, the gasket is firmly adsorbed on the turntable through the magnetic force, and position deviation of the gasket due to inertia is effectively prevented; and it is ensured that magnetic force is evenly distributed below the gaskets, the stability of the gaskets in the transporting process is further improved, and reliable guarantee is provided for subsequent detection work.

The invention discloses a video face irreversible privacy processing method, edge computing equipment and a video face irreversible privacy processing system, and belongs to the technical field of video data processing and privacy computing. The method comprises the following steps: receiving YUV naked stream data at an edge side; performing face detection and behavior recognition, and determining a target face region and a behavior category; extracting a corresponding face feature vector; after the face feature vector is extracted, performing unrecoverable elimination on the original pixel data of the target face area immediately; performing irreversible hash operation on the face feature vector to generate an anonymous hash value; immediate unrecoverable removal is carried out on the face feature vector; and packaging the anonymous hash value and the behavior category information into a micro data packet, and uploading the micro data packet to a cloud server. According to the invention, through the irreversible hash and instant memory zero clearing technology, face pixels and feature information are destroyed at the edge side, and the cloud cannot be recovered, so that the compliance requirement of'unrecoverable 'is completely met.

The utility model relates to the technical field of sorting equipment, in particular to an automatic detection sorting device which comprises a workbench, a conveying belt is arranged in the workbench, a plurality of detection piece bodies are placed on the conveying belt, a detection frame is fixedly installed on the workbench, and sliding grooves are formed in the two ends of the detection frame. The device comprises a workbench, a detection frame is installed on the workbench, a turnover bearing assembly is slidably installed at the lower end of the detection frame, side face detection probes are distributed and fixedly installed on the workbench, and a top face detection probe is fixedly installed at the top of the detection frame. The bottom of the detection piece body is detected, meanwhile, comprehensive detection of the detection piece body can be achieved through the side face detection probe and the top face detection probe which are arranged in a distributed mode, unqualified detection piece bodies after detection can be reasonably classified through cooperation of an electric push rod and a push plate, and practicability is high.

Disclosed is a method, system and device for estimating the three-dimensional gaze direction of a human eye based on appearance. The method comprises: acquiring an image or video containing a face; performing face detection in the image or video frame, introducing a face key point detection algorithm to detect and locate the face key points; cutting out the face image and left and right eye images according to the face detection result and the positions of the key points; and estimating the three-dimensional gaze of the human eye in the image using a gaze estimation model, wherein the training process of the gaze estimation model comprises: using a Base-CNNs network to extract features of the face image to obtain a full-face image feature vector, using two asymmetric branch structures of an EE-Net network to extract features of the left and right eye images to obtain double-eye image feature vectors, estimating a head posture vector in combination with the face key point detection algorithm, and finally using a full connection layer to regress the full-face image feature vector, the double-eye image feature vector and the head posture vector for the final gaze estimation task.

The application discloses a kind of steel bar end face detection systems based on deep learning, comprising: data acquisition and pre-processing module, for collecting steel bar end face image, generates end face structure state set;Improved NTK processing module, for constructing improved NTK, generates end face structure kernel mapping result;Structure accessibility analysis module, for constructing end face structure transformation path set, generates structure accessibility measurement matrix;Kernel self-driving module, for performing kernel self-driving evolution, generates kernel stable solution;Cross-sample coupling module, for establishing cross-sample kernel coupling relationship, generates group consistency constraint result;Kernel fission recombination module, for splitting and recombination, generates recombination kernel response result;Detection result generation module, for generating steel bar end face structure detection result.The application is based on improved NTK and structure path modeling, realize steel bar end face multidimensional feature fusion detection, with the advantages of high precision, strong stability and strong complex defect recognition ability.

The application provides a driver emotion recognition model adaptive updating method and device and electronic equipment, constructs texture feature vectors and geometric feature vectors based on face detection results of a driver face video frame sequence and carries out fusion, carries out emotion recognition through a personalized emotion recognition model of a vehicle end, carries out multi-dimensional credibility evaluation based on emotion recognition results, marks out difficult samples based on multi-dimensional credibility evaluation results and preset threshold information and iteratively updates parameters of the personalized emotion recognition model, desensitizes target data corresponding to the difficult samples, and sends the desensitized data and the updated parameters of the personalized emotion recognition model to the cloud end, so that the cloud end sends the global emotion recognition model after updating to the vehicle end. The application can improve the accuracy and stability of the vehicle end emotion recognition, and ensure the universality of the vehicle end for accurately recognizing the emotions of different drivers.

The invention relates to the technical field of intelligent management, and discloses a resort intelligent management platform, which comprises a monitoring acquisition module for acquiring a working area picture in real time and formulating a preliminary acquisition period duration, an information identification module for identifying a personnel face image in the preprocessed working area picture based on a face detection and identification combined model, extracting facial feature vectors; the method comprises the following steps: identifying personnel identity information based on Euclidean distance, determining whether the personnel is a worker or not, and identifying whether the dressing of the worker meets the standard or not when the personnel is identified, optimizing an acquisition period according to the statistical condition of identification data in an identification period, and performing early warning by an information early warning module when no worker appears in the acquisition period. And when the dressing information of the worker does not meet the standard, an alarm is given. According to the invention, the dynamic management of the personnel identity information is realized, and the actual operation management requirement is met.

This application provides a text-guided method and system for detecting face forgery. The method includes obtaining a face image to be detected (whether it is genuine or fake), inputting the face image into a face detection model to obtain the face authenticity detection result. The face detection model includes: constructing a text prompt lexicon covering multiple granularities and generating multi-dimensional text prototypes; extracting visual features, optimizing the feature distribution of visual features to obtain global visual features; performing feature separation and enhancement on the global visual features; mapping the global visual features after feature separation and enhancement to predicted text features; applying similarity constraints to obtain cross-modal prototype matching results; applying discriminative constraints on different-dimensional text prototypes to obtain feature measurement learning results; and outputting the detection result based on the cross-modal prototype matching results and feature measurement learning results. This method solves the problem of poor generalization in face forgery detection and improves detection accuracy and generalization ability.

The present application provides a kind of face image evaluation method, device, equipment and readable storage medium, face image evaluation method includes: using face detection model to detect the image to be detected, obtain the face frame and attitude angle of all faces to be detected in the image to be detected;For each face to be detected, determine the face image of the face to be detected according to the face frame of the face to be detected;Using face quality evaluation model to evaluate face image, obtain the score of face image;According to the score of face image and the corresponding attitude angle, the comprehensive score of face image is calculated.The present application detects the face frame and attitude angle of all faces in face detection model, and the face quality evaluation model no longer detects the attitude angle, so that the efficiency of the face quality evaluation model can be greatly improved in the case of multiple faces in the snapshot image needing to be evaluated separately, thereby meeting the application requirements of multiple face real-time snapshot even on embedded devices.