1048 results about "Facial characteristic" patented technology

A multi-modal articulation system translates human gestures into control commands for digital and automation devices. The system integrates a camera module, which captures facial and hand movements, with an image processing unit that extracts facial features, such as eyebrow movement, lip curvature, eyelid motion, and eye trajectory. These extracted features are analyzed in real time by an articulation recognition module that compares them against a database to recognize specific gestures. The system tracks and processes air-drawn hand gestures using a gesture trajectory tracking unit, which converts the motions into digital representations and classifies them using deep learning algorithms. The recognized gestures are converted into machine-readable control signals and executed on various connected devices via a relay control unit. The system incorporates adaptive algorithms to improve accuracy, account for environmental conditions, and stabilize hand tremors, providing a gesture-based control interface for applications ranging from smart home systems to multimedia devices.

The invention relates to the field of road traffic, in particular to a multi-feature fusion fatigue driving detection method and a fatigue driving detection system. The method comprises the following steps: extracting facial features from a face image of a driver; extracting vehicle features from the vehicle driving parameters of the vehicle driven by the driver; and fusing the facial features and the vehicle features to judge whether the driver is in fatigue driving. Extracting facial features by designing a CNN model; extracting basic convolution features; extracting local convolution features; extracting global convolution features; performing pooling operation; aggregating global features; and carrying out dimensionality reduction mapping. A self-encoder is designed to extract vehicle characteristics; a symmetric deep neural network structure is adopted, and high-dimensional time sequence data is compressed to a low-dimensional potential space through nonlinear mapping; through combination and matching of the CNN model and the auto-encoder, the technical defects of feature redundancy, noise interference, information loss and suboptimal decision existing in an existing multi-feature fusion fatigue driving detection system are thoroughly solved.

The invention provides a multi-mode-based man-machine interaction method and system, and relates to the technical field of man-machine interaction. Voice input of a user is received, environment information is collected, facial expressions of the user are captured, and voice features, environment features and facial features are obtained based on the input; utilizing a pre-trained emotion recognition model to generate emotion features of the user, and utilizing a pre-trained user behavior model to generate behavior features according to the current operation information of the user; after the features are obtained, the first weights and the second weights of the features are dynamically adjusted, feature weighted fusion is carried out, and comprehensive features are obtained; the multiple candidate responses are generated based on the comprehensive characteristics, the final response is screened out through a preset scoring mechanism and output, comprehensive understanding of the intention and the emotional state of the user is achieved through comprehensive utilization of multiple data sources, and meanwhile the weight of each characteristic can be dynamically adjusted so that more intelligent and humanized interaction experience can be generated.

The invention discloses a pig individual identification and health monitoring system based on biological characteristics, and relates to the technical field of medical treatment, and the system comprises a multi-mode biological characteristic collection module which is used for obtaining pig 3D facial characteristics, ear blood vessel distribution characteristics, gait dynamic characteristics and physiological data; the biological feature fusion recognition module is used for fusing multi-modal features based on a Transform architecture and realizing individual unique identification and feature template dynamic updating; and the digital twinborn health monitoring module is used for constructing a pig full-life-cycle digital twinborn model and realizing health state evaluation and disease early warning in combination with an LSTM neural network. According to the pig individual recognition and health monitoring system based on the biological characteristics, 1024-dimensional standardized feature vectors are constructed through cooperation of multiple devices such as an RGB-D camera, a near-infrared camera and a binocular vision sensor, modal weights are dynamically distributed in combination with a Transform network, and high-precision recognition in a complex scene is achieved.

The invention relates to the technical field of audio and video identity recognition, in particular to an audio and video identity recognition system based on multimode clue driving. The audio and video identity recognition system comprises an audio feature extraction module, a video feature extraction module, a multimode clue fusion module, a living body detection module and an identity recognition and verification module. The audio feature extraction module is used for capturing a voice signal of a user and extracting key voiceprint features, the video feature extraction module is used for acquiring facial features or limb features of the user and extracting related features, and the multi-mode clue fusion module is used for performing intelligent weighted fusion on the features of audio and video modes. The method comprises the following steps: firstly, using a living body detection module to comprehensively analyze the dynamic characteristics of audio and video, judging whether a user is a real individual, and finally, completing the final verification of the identity of the user through an identity recognition and verification module. The problem of feature extraction and modal fusion in the prior art is effectively solved.

The invention belongs to the technical field of face recognition, and particularly relates to a face recognition algorithm adaptive to illumination variation, which uses Gaussian filtering to reduce noise caused by illumination variation, convert a color image into a grey-scale image, reduce calculation complexity, perform adaptive histogram equalization, estimate global illumination conditions in the image and improve the face recognition accuracy. Calculating the main direction and intensity of illumination in the image, predicting the current illumination condition, and enabling the image brightness to adapt to different environment illumination; identifying a face region in the image, using a deep learning method to position face related points, using an LBP to extract local features insensitive to illumination variation, and using a deep convolutional neural network to extract global features; introducing a multi-illumination data set; the Euclidean distance is used for comparing the extracted feature vectors, the similarity between the extracted feature vectors and known faces in a database is recognized, according to the similarity score, threshold judgment is adopted for AQW to obtain a final recognition decision, and the method has the effect of being capable of accurately adapting to facial features under different illumination conditions in real time.

The invention discloses a campus face recognition abnormal behavior monitoring method and system based on artificial intelligence, and the method comprises the steps: collecting the video stream data of a person in a campus in real time, and recognizing the behavior characteristics of the person in a video frame in real time through employing a lightweight target detection algorithm; extracting facial features of the personnel by using a pre-trained face recognition model, and recognizing identity information of the personnel; generating a personnel behavior semantic description vector with a spatio-temporal context based on the behavior characteristics and the identity information; inputting the personnel behavior semantic description vector into a dynamic early warning threshold engine, and outputting an abnormal behavior probability and risk level evaluation result; and triggering an early warning information pushing mechanism according to the abnormal behavior probability and the risk level evaluation result, and sending early warning information to a corresponding responsible person terminal. By using the embodiment of the invention, the accurate and automatic association of the abnormal behavior and the personnel identity can be realized, and the early warning accuracy, the handling response speed and the system adaptive ability of campus safety monitoring are improved.

The invention provides an interactive classroom student dynamic portrait generation method, a medium and a system, and belongs to the technical field of student dynamic portrays.The method comprises the steps that a student facial image sequence is collected, facial feature points are extracted to construct an expression feature matrix, and sight line focus data are obtained by combining an eye movement tracking technology to calculate an eye light concentration degree index; and establishing an individual association model, identifying specific expressions and analyzing attention fluctuation characteristics. And fusing multi-dimensional features such as an expression similarity vector and an eye light concentration index into a dynamic attention state score. Learning state evaluation is realized based on a multilayer bidirectional converter network, wherein multi-head attention mechanism parameters are determined by expression change frequency, an eye light concentration threshold and an attention fluctuation rate. And finally, generating a student dynamic portrait feature map, and forming a classroom interaction effect evaluation index through clustering analysis, thereby solving the technical problem that the dynamic change of the student classroom attention state is difficult to accurately evaluate based on a single feature.

The invention discloses a face recognition system based on computer vision and an application method thereof, and relates to the technical field of face recognition. The system comprises a fine-grained shielding classification module, a multi-modal feature fusion module, a cross-modal confrontation identification module and a compensation verification module. By analyzing the depth data and the polarized light parameters, an occlusion thermodynamic diagram is generated, and an identifiable area and an occlusion area are marked. And according to the occlusion label of the thermodynamic diagram, activating infrared and sonic sensor data, extracting the relevance between the temperature change and the vibration frequency, and generating a facial occlusion area feature map. And calculating the similarity between the sheltered region feature map and the non-sheltered region feature, and detecting a forgery attack. And analyzing the difference rate of adjacent frames through a sliding window, marking compensation distortion, rejecting low-credibility data in combination with confidence and attack marks, and outputting a complete facial feature vector and a risk level. And the recognition accuracy and the system security in a complex environment are remarkably improved.

The invention discloses an automobile data recorder fatigue driving detection and reminding method based on multi-modal data fusion, and the method comprises the following steps: S1, collecting and preprocessing a face image through a vehicle-mounted camera, and carrying out the face detection and region positioning; s2, dynamically adjusting the size and the position of an image block by adopting a dynamic image blocking technology; s3, performing facial image feature extraction by using a visual converter; s4, acquiring vehicle multi-modal data and fusing the vehicle multi-modal data with the facial features; s5, optimizing a visual converter network structure and training hyper-parameters through a wolf pack optimization algorithm; s6, performing fatigue state analysis based on the optimized visual converter; and S7, when the fatigue state is detected, reminding the driver to have a rest through sound-light alarm. The fatigue driving detection and reminding method of the automobile data recorder is high in precision and high in real-time performance through a deep learning algorithm of multi-modal data fusion and optimization, and the recognition accuracy of fatigue driving and the robustness of the system are effectively improved.

The invention discloses an automatic conference summary generation method based on multi-source heterogeneous information fusion. The method comprises the following steps: extracting facial features of a speaker by using a face recognition technology and recognizing a face identity of the speaker; acquiring an audio signal through a microphone array, and recognizing a voiceprint identity of a speaker by using a voiceprint recognition technology; in combination with video and audio information, through a multi-source heterogeneous information fusion technology, alignment of video and audio data is carried out in time; through a sound source positioning technology, a voiceprint identity and a face identity are matched and aligned in space, and the identity of a speaker is accurately positioned and recognized; the spokesman and the speaking content are calibrated and separated, and it is ensured that the identity of the spokesman is accurately matched with the speaking content; and generating a spokesman abstract and a conference summary according to the calibrated speech content by using natural language processing and a deep learning model. The method is suitable for various conference scenes, the speaking content of different spokesmen can be recognized, the spokesman abstract is generated, and the efficiency and accuracy of conference summary generation are improved.

The invention discloses a multi-modal emotion recognition method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring text, video and audio data of a user and respectively performing feature extraction to obtain text features, audio features and facial features; the three features are input into a pre-trained multi-modal emotion recognition model, the multi-modal emotion recognition model comprises a first fusion module, a second fusion module, a third fusion module, a fourth fusion module and a classification module, the audio features and the text features are fused through the first fusion module, and audio text features are obtained; fusing the facial features and the text features by using a second fusion module to obtain facial text features; performing feature enhancement on the text features by using a third fusion module to obtain enhanced text features; fusing the audio text features, the face text features and the enhanced text features by using a fourth fusion module to obtain multi-modal features; and classifying the multi-modal features by using a classification module to obtain a sentiment classification result of the user.

The invention relates to the technical field of identity authentication, and discloses an identity authentication method and system based on a large-model multi-mode venue, and the method comprises the steps: obtaining a fingerprint image, a face image and behavior interaction data of a user; performing fingerprint feature extraction according to the fingerprint image to obtain a fingerprint feature vector; performing facial feature extraction according to the facial image to obtain a facial feature vector; performing feature fusion on the fingerprint feature vector and the face feature vector, and generating a fusion feature vector after data dimension reduction; performing behavior feature extraction according to the behavior interaction data to obtain a behavior feature vector; and after cross-modal mapping is carried out according to the fusion feature vector and the behavior feature vector, matching degree analysis is carried out in a preset unified semantic space, and an identity authentication result is obtained. The method has the following effect that the identification security of identity authentication can be improved.

A system for providing privacy measures for a computing device comprising: a memory a display configured to display images and / or data which may be subject to privacy concerns; at least one image sensor configured to provide image data of a user operating the computing device and image data of the environment surrounding the user within an image sensor field of view; at least one processor configured to: receive the image data to segment the image data to isolate the facial features of a person comprising a user and generate a user facial vector representative of characteristics of the segmented facial image data; and compare the user facial vector with facial vector data associated with one or more authorised device users stored in the memory; and a privacy controller module configured to determine a privacy threat alert based on the facial vector comparison; wherein, on receipt of a privacy threat alert from the privacy controller, the processor is configured to modify the display of the electronic device to preserve the privacy of the displayed image and / or data.

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.

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

The invention discloses an angry emotion recognition method based on a video stream, and belongs to the technical field of emotion recognition. The method comprises the following steps: preprocessing a video stream of a visitor to obtain a time-space aligned standardized facial image sequence, a 3D skeleton sequence and a voice segment; performing feature extraction on the standardized facial image sequence by using a 3D-ResNet34 network and a micro-expression optical flow enhancement algorithm to obtain optical flow facial features; performing feature extraction on the 3D skeleton sequence by using a space-time diagram convolutional network to obtain limb action features; performing feature extraction on the voice segments to obtain voice features; and performing fusion processing on the facial features, the body movement features and the voice features by using a multi-head space-time cross attention mechanism and an expansion time sequence convolution algorithm, and outputting an angry emotion recognition result through dynamic weight distribution classification. According to the method, the angry emotion recognition accuracy of the system is greatly improved on the whole.

The invention relates to the technical field of identity continuous verification, and particularly discloses an Internet of Things identity continuous verification method based on a multi-mode technology, which comprises the following steps: continuously collecting face image data and keyboard input mode data of a user in an interaction process of the user and Internet of Things equipment; the deep learning technology is adopted to process the face image data and the keyboard input mode data of the user to extract the face semantic features and the keyboard use behavior mode features of the user, and then cross-modal association learning interaction fusion is carried out on the face features and the keyboard use behavior mode features of the user, so that the user experience is improved. Therefore, the potential association between the two is captured, and the continuous verification of the user identity is realized. Through the mode, the behavior of the user can be continuously monitored, and an alarm is given out in time when the identity of the user is abnormal, so that the safety of the Internet of Things equipment is effectively improved, and meanwhile, the problem that the user experience is reduced possibly caused by a traditional continuous verification method is avoided.

The invention discloses a facial emotion analysis method based on spatial-temporal feature network construction. The method comprises the following steps: S1, video frame segmentation and feature extraction; s2, dividing the video into a plurality of time periods according to the set time window length and time step length; and for each time period, constructing a facial feature network representing association between facial features based on the feature sequences of all frames in the time period. The networks obtained through calculation in all the time periods are synthesized, and a dynamic network sequence is obtained; s3, performing dynamic network feature extraction and dynamic key point analysis based on the facial feature dynamic network; and S4, performing emotion calculation or psychological state evaluation based on the dynamic network features or the dynamic key points. The method solves the problems that an existing method is difficult to comprehensively capture complex dynamic characteristics of facial expressions and lacks dynamic correlation modeling and dynamic key point recognition capabilities.

The invention relates to the technical field of intelligent detection, in particular to an edge fatigue detection method and device based on adaptive threshold adjustment and a medium. The method comprises the following steps: acquiring facial image data and facial infrared data of a driver in a real-time state; preprocessing the face image data and the face infrared data; performing facial mark point positioning on the preprocessed facial image data and facial infrared data through a facial feature detection model to obtain an enhanced 3D mark point image; comparing the enhanced 3D mark point image with a physiological feature database to obtain physiological data; and calculating the physiological data through a dynamic threshold decision model and a multi-index fusion engine to obtain a fatigue decision. According to the method, the environmental adaptability can be improved, individualized precise adaptation is realized, precise adaptation is realized for individual differences, and the multi-working-condition robustness is enhanced.

The invention relates to the technical field of bionic humanoid robots, in particular to a multi-expression driving bionic intelligent robot head which comprises an eyebrow movable layer, a cheekbone movable layer, an eye movable layer, an upper lip movable layer and a chin movable layer located below the upper lip movable layer, and the eyebrow movable layer, the cheekbone movable layer, the eye movable layer and the upper lip movable layer are spliced and assembled by hexagonal threaded copper columns through reserved hole positions. The robot head is composed of a shell, an aluminum alloy machining plate frame, a CNC metal part, a steering engine, a rail and a pull wire, and a camera module and a voice control module are arranged in the robot head. The shell can be customized according to different figure facial features, the shell is of a high-precision, compact and adjustable structure, 26 steering engines are integrated in a limited space to control opening and closing of the face including left and right eyeballs, upper and lower eyelids, canthus, eyebrows, cheeks, mouth corners and mouth and actions of lips, and the layered design and control mode of the head of the robot enable the robot to be more intelligent. And the mounting, the dismounting and the maintenance are more convenient.

The invention discloses a face recognition method and system based on cross-age features, belongs to the technical field of face recognition, and is used for solving the problems that traditional face recognition is slow in old people who need to see a doctor through face recognition, slow recognition and recognition errors are easily caused for people with large age spans, and the face recognition efficiency is high. And the medical seeing efficiency of the user is influenced. The method comprises the following steps: performing marking processing on cortical layer texture features and variable epidermal layer features on an acquired initial facial image, and determining a facial cortex feature image; carrying out dynamic stripping processing on variable epidermal layer features in the facial cortex feature image, and carrying out physical optical constraint on dermal layer texture features; carrying out extraction processing on facial feature points under related topological structures and shapes on the optimized facial image; performing age attribute editing diffusion processing on the optimized face image; and performing feature dimension reduction processing on the face key features, and determining social security identity information of the medical seeking personnel.

The invention discloses a face image reconstruction method based on semantic identity feature decoupling and consistency reservation of a diffusion model, and the method comprises the steps: 1, obtaining and preprocessing a face image set of identity labeling, and generating a face feature point distribution diagram, a semantic mask diagram and a description text; 2, multi-modal features are extracted and fused through a semantic identity extraction network; 3, carrying out noise adding and de-noising processing by utilizing a diffusion model, and combining a reconstructed network and semantic identity loss optimization; and 4, face image reconstruction is completed. According to the method, in the face image reconstruction process, the driving requirements of semantic information such as texts for image editing can be accurately captured, fine-grained semantic features and identity features are decoupled, the core identity features of the face can be effectively reserved, and loss of identity consistency caused by semantic editing is avoided; therefore, technical support is provided for application scenes with high requirements on face identity accuracy in the field of computer vision, and the reliability and practicability of face image reconstruction are improved.

The invention relates to the technical field of identity verification, in particular to an intelligent lock unlocking method and system based on face image processing, and the method comprises the following steps: obtaining face images of multiple angles through an image collection device, extracting face key features of a user, including a face contour, face symmetry and positions of five sense organs, and generating a face feature data set; according to the method, the limitation of traditional two-dimensional plane recognition is broken through through acquisition of multi-angle face images, construction of a multi-dimensional feature data set, realization of feature fusion by utilizing difference evaluation and weight distribution, enhancement of the stability and the anti-interference capability of biological feature information and extraction of three-dimensional geometric information and depth data; and in combination with a dynamic illumination threshold monitoring mechanism, the influence of environment variables on recognition precision is suppressed, and the resistance to plane attacks is improved, so that the security level and scene adaptability are both improved while the convenience of the identity authentication system is kept.

An AI-driven emotion recognition system for learning adaptation in educational institutions, comprehensive: • an emotion recognition unit that tracks students' facial expressions in real time and categorizes the resulting expressions into a specific emotional class from a given set using a Convolutional Neural Network (CNN) trained on a large emotion dataset; • a real-time processing unit that extracts facial features and dynamic emotional characteristics of students through adaptive filter algorithms; • an LMS integration unit, adapted to combine discovered emotional knowledge with online and offline learning environments for customizable learning adjustments; • a personalization engine programmed to intelligently adapt training content, response approaches, and training materials according to the identified emotional states, so that each learner receives individually tailored and more intensive learning experiences; • a feedback loop that sends educators real-time alerts and recommended videos to address emotional feedback or lesson gaps, allowing teachers to tackle negative signals in the moment (e.g., frustrated face, etc.); • AI data storage and adaptive learning model unit that stores anonymized data for emotion recognition, updates the AI ​​model and improves it through learning over time, and ensures that the use of the information is in compliance with data protection regulations; • A user interface module specifically designed for educators, providing a visual overview of students' emotional patterns and real-time analysis of their emotions and engagement as a basis for important decisions that lead to improvements in teaching processes.

The invention discloses a diagnostic wax pattern digital manufacturing method and system, a product and a medium, and relates to the field of image processing.The method comprises the steps that oral cavity data of a target patient are obtained according to a three-dimensional oral cavity scanner, facial feature data of the patient are synchronously collected through a facial scanner, and the oral cavity data are converted into a first digital model; converting the facial feature data into a second digital model, extracting key parameters of teeth of the target patient based on the first digital model, marking a to-be-modified area on the first digital model based on the personalized demand information, extracting a key control point in the to-be-modified area, extracting a repair parameter standard value from a clinical case database, and repairing the target patient according to the repair parameter standard value. Constructing an initial digital veneering model based on the parameter standard values; and converting the personalized demand information into a target adjustment amount of the key control point, and modifying the initial digital veneering model according to the target adjustment amount to obtain a digital diagnosis wax pattern. By implementing the method, the manufacturing accuracy of the oral cavity repairing and diagnosing wax pattern can be improved.

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 discloses a driver fatigue state detection system based on computer vision processing. The system comprises a facial feature extraction unit which selects a high-definition camera to capture a driver facial image; the upper body feature monitoring unit collects upper body image data through a high-definition camera to accurately extract the action frequency of the upper body; the hand-held steering wheel characteristic monitoring unit is used for sensing pressure applied by hands of a driver by arranging a grip strength sensor on a steering wheel through a protective sleeve; the data processing unit receives data information collected by the facial feature extraction unit, the upper body feature monitoring unit and the handheld steering wheel feature monitoring unit for feature fusion; the fatigue state judgment unit judges the fatigue state of the driver according to a preset fatigue judgment rule; the face blinking frequency is combined with multi-dimensional information such as the upper body action frequency and the grip strength, the state of a driver can be reflected from different angles, richer and more comprehensive data support is provided for fatigue judgment, and the fatigue state can be judged more accurately.

The invention discloses a student state real-time analysis method and device based on deep learning, and the method comprises the steps: obtaining and preprocessing image data of students in a classroom, and carrying out the multi-scale face detection and facial feature extraction of the preprocessed image data; expression features are extracted based on standardized facial feature data, fusion is carried out in combination with attention indexes and attitude features, a time sequence feature sequence is constructed, a heavy-tailed recurrent neural network is applied to carry out time sequence modeling, an evaluation standard is established, evaluation parameters are adjusted through a self-adaptive threshold value, and finally a student state evaluation result is obtained. Through the heavy-tailed recurrent neural network and a slow transition mechanism to low-dimensional chaos, subtle changes and long-term trends of student states can be accurately captured, and the technical problems that a traditional student state monitoring method is poor in real-time performance, limited in coverage and insufficient in individuation are solved.

The invention discloses a quick start-up control method of a face recognition intelligent lock system, which comprises the following steps: enabling the face recognition intelligent lock system to be in an ultra-low power consumption sleep mode in which only a trigger sensing unit is kept for power supply in a normal state, and when the trigger sensing unit detects that a person approaches, executing hardware-level quick wake-up processing of skipping a redundancy self-checking process on the system, entering a system wakeup initialization completion state to execute core module preloading and algorithm component ready processing of preloading a human face recognition core algorithm component on the human face recognition intelligent lock system, and entering a human face recognition module ready state to enable the core module to execute facial feature collection and compare with a local feature library; if the comparison is passed, a power supply self-holding circuit is triggered, and power supply self-holding and unlocking driving linkage processing of maintaining power supply of an unlocking driving unit to complete unlocking and cutting off a power supply of a face recognition unnecessary module is executed on the face recognition intelligent lock system, so that the face recognition intelligent lock system enters a dormant state of unlocking completion and system low consumption.