5893 results about "Face perception" patented technology

Here, we introduce Z-webs, including Z-factors and Z-nodes, for the understanding of relationships between objects, subjects, abstract ideas, concepts, or the like, including face, car, images, people, emotions, mood, text, natural language, voice, music, video, locations, formulas, facts, historical data, landmarks, personalities, ownership, family, friends, love, happiness, social behavior, voting behavior, and the like, to be used for many applications in our life, including on the search engine, analytics, Big Data processing, natural language processing, economy forecasting, face recognition, dealing with reliability and certainty, medical diagnosis, pattern recognition, object recognition, biometrics, security analysis, risk analysis, fraud detection, satellite image analysis, machine generated data analysis, machine learning, training samples, extracting data or patterns (from the video, images, and the like), editing video or images, and the like. Z-factors include reliability factor, confidence factor, expertise factor, bias factor, and the like, which is associated with each Z-node in the Z-web.

The invention discloses a human face super-resolution reconstruction method based on a generative adversarial network and sub-pixel convolution, and the method comprises the steps: A, carrying out the preprocessing through a normally used public human face data set, and making a low-resolution human face image and a corresponding high-resolution human face image training set; B, constructing the generative adversarial network for training, adding a sub-pixel convolution to the generative adversarial network to achieve the generation of a super-resolution image and introduce a weighted type loss function comprising feature loss; C, sequentially inputting a training set obtained at step A into a generative adversarial network model for modeling training, adjusting the parameters, and achieving the convergence; D, carrying out the preprocessing of a to-be-processed low-resolution human face image, inputting the image into the generative adversarial network model, and obtaining a high-resolution image after super-resolution reconstruction. The method can achieve the generation of a corresponding high-resolution image which is clearer in human face contour, is more specific in detail and is invariable in features. The method improves the human face recognition accuracy, and is better in human face super-resolution reconstruction effect.

A method and apparatus for facial image acquisition and / or recognition used for person identification. In infrared face image acquisition, near infrared (NIR) images of a face are captured by an imaging unit with the face illuminated by active NIR lights; an NIR optical filter is used in the imaging unit to minimize visible lights in environments while allowing NIR lights to pass through. NIR face images thus acquired provides good image quality for the purpose of face recognition. In face recognition, eyes are localized in NIR face image(s) quickly and accurately by detecting specular highlight reflection in each eye, whereby face is then localized. The invention effectively problems caused by environmental lights, and leads to accurate and fast face recognition under variable lighting conditions. Moreover, the methods use a non-intrusive and user-friendly way of active lighting for face image acquisition and recognition because the NIR lights are in the invisible spectrum.

Disclosed herein is a system and method for iris recognition including stereoscopic face recognition, which can recognize irises including stereoscopic face recognition system in order to recognize an authenticatee. The system includes two or more face recognition cameras for photographing two or more face images of an authenticatee; a recognition system for receiving the face images photographed by the face recognition cameras from the face recognition cameras and creating stereoscopic face information on the basis of the face images; and one or more iris recognition cameras controlled by the recognition system to photograph focused irises of the authenticatee using the created stereoscopic face information.

Here, we introduce Z-webs, including Z-factors and Z-nodes, for the understanding of relationships between objects, subjects, abstract ideas, concepts, or the like, including face, car, images, people, emotions, mood, text, natural language, voice, music, video, locations, formulas, facts, historical data, landmarks, personalities, ownership, family, friends, love, happiness, social behavior, voting behavior, and the like, to be used for many applications in our life, including on the search engine, analytics, Big Data processing, natural language processing, economy forecasting, face recognition, dealing with reliability and certainty, medical diagnosis, pattern recognition, object recognition, biometrics, security analysis, risk analysis, fraud detection, satellite image analysis, machine generated data analysis, machine learning, training samples, extracting data or patterns (from the video, images, and the like), editing video or images, and the like. Z-factors include reliability factor, confidence factor, expertise factor, bias factor, and the like, which is associated with each Z-node in the Z-web.

A face recognition system and process for identifying a person depicted in an input image and their face pose. This system and process entails locating and extracting face regions belonging to known people from a set of model images, and determining the face pose for each of the face regions extracted. All the extracted face regions are preprocessed by normalizing, cropping, categorizing and finally abstracting them. More specifically, the images are normalized and cropped to show only a person's face, categorized according to the face pose of the depicted person's face by assigning them to one of a series of face pose ranges, and abstracted preferably via an eigenface approach. The preprocessed face images are preferably used to train a neural network ensemble having a first stage made up of a bank of face recognition neural networks each of which is dedicated to a particular pose range, and a second stage constituting a single fusing neural network that is used to combine the outputs from each of the first stage neural networks. Once trained, the input of a face region which has been extracted from an input image and preprocessed (i.e., normalized, cropped and abstracted) will cause just one of the output units of the fusing portion of the neural network ensemble to become active. The active output unit indicates either the identify of the person whose face was extracted from the input image and the associated face pose, or that the identity of the person is unknown to the system.

A robot includes a face extracting section for extracting features of a face included in an image captured by a CCD camera, and a face recognition section for recognizing the face based on a result of face extraction by the face extracting section. The face extracting section is implemented by Gabor filters that filter images using a plurality of filters that have orientation selectivity and that are associated with different frequency components. The face recognition section is implemented by a support vector machine that maps the result of face recognition to a non-linear space and that obtains a hyperplane that separates in that space to discriminate a face from a non-face. The robot is allowed to recognize a face of a user within a predetermined time under a dynamically changing environment.

The invention discloses a face recognition method, device, system and apparatus based on a convolutional neural network (CNN). The method comprises the following steps: S1, face detection: using a multilayer CNN feature architecture; S2, key point positioning: obtaining the key point positions of the face by using connecting reference frame regression networks in a cascaded way in deep learning; S3, preprocessing: obtaining a face image with a fixed size; S4, feature extraction: obtaining a feature representative vector by means of a feature extraction model; and S5: feature comparison: determining the similarity according to a threshold or giving a face recognition result according to distance sorting. The method adds a multilayer CNN feature combination to a traditional CNN single-layer feature architecture to treat different imaging conditions, trains a face detection network with good robustness in the monitoring environment from massive image data sets based on a deep convolution neural network algorithm, reduces a false detection rate, and improves detection response speed.

A method and apparatus for recognizing and searching for a face using 2nd-order independent component analysis (ICA) are provided. The method for describing feature points uses 2nd-order ICA d to describe a facial image space and improve recognition performance in various illumination conditions. According to the method and apparatus, use of pose or illumination invariant face descriptor enables retrieval of human faces and authentication of a specific individual.

Face recognition of a face, to determine whether the face correlates with an enrolled face, may include generating a personalized three-dimensional (3D) face model based on a two-dimensional (2D) input image of the face, acquiring 3D shape information and a normalized 2D input image of the face based on the personalized 3D face model, generating feature information based on the 3D shape information and pixel color values of the normalized 2D input image, and comparing the feature information with feature information associated with the enrolled face. The feature information may include first and second feature information generated based on applying first and second deep neural network models to the pixel color values of the normalized 2D input image and the 3D shape information, respectively. The personalized 3D face model may be generated based on transforming a generic 3D face model based on landmarks detected in the 2D input image.

A novel method and system for 3d-aided-2D face recognition under large pose and illumination variations is disclosed. The method and system includes enrolling a face of a subject into a gallery database using raw 3D data. The method also includes verifying and / or identifying a target face form data produced by a 2D imagining or scanning device. A statistically derived annotated face model is fitted using a subdivision-based deformable model framework to the raw 3D data. The annotated face model is capable of being smoothly deformed into any face so it acts as a universal facial template. During authentication or identification, only a single 2D image is required. The subject specific fitted annotated face model from the gallery is used to lift a texture of a face from a 2D probe image, and a bidirectional relighting algorithm is employed to change the illumination of the gallery texture to match that of the probe. Then, the relit texture is compared to the gallery texture using a view-dependent complex wavelet structural similarity index metric.

A system and method for determining a classifier to discriminate between two classes—object or non-object. The classifier may be used by an object detection program to detect presence of a 3D object in a 2D image (e.g., a photograph or an X-ray image). The overall classifier is constructed of a sequence of classifiers (or “sub-classifiers”), where each such classifier is based on a ratio of two graphical probability models (e.g., Bayesian networks). A discrete-valued variable representation at each node in a Bayesian network by a two-stage process of tree-structured vector quantization is discussed. The overall classifier may be part of an object detector program that is trained to automatically detect many different types of 3D objects (e.g., human faces, airplanes, cars, etc.). Computationally efficient statistical methods to evaluate overall classifiers are disclosed. The Bayesian network-based classifier may also be used to determine if two observations (e.g., two images) belong to the same category. For example, in case of face recognition, the classifier may determine whether two photographs are of the same person. A method to provide lighting correction or adjustment to compensate for differences in various lighting conditions of input images is disclosed as well. As per the rules governing abstracts, the content of this abstract should not be used to construe the claims in this application.

A video tagging method and a video apparatus using the video tagging method are provided. The video apparatus includes a player module which plays a video; a face recognition module which recognizes a face of a character in the video; a tag module which receives a tagging key signal for tagging a scene of the video including the character and maps a tagging key corresponding to the tagging key signal and a number of scenes including the face recognized by the face recognition module; and a storage module which stores the result of mapping performed by the tag module.

The face detection system and method attempts classification of a test image before performing all of the kernel evaluations. Many subimages are not faces and should be relatively easy to identify as such. Thus, the SVM classifier try to discard non-face images using as few kernel evaluations as possible using a cascade SVM classification. In the first stage, a score is computed for the first two support vectors, and the score is compared to a threshold. If the score is below the threshold value, the subimage is classified as not a face. If the score is above the threshold value, the cascade SVM classification function continues to apply more complicated decision rules, each time doubling the number of kernel evaluations, classifying the image as a non-face (and thus terminating the process) as soon as the test image fails to satisfy one of the decision rules. Finally, if the subimage has satisfied all intermediary decision rules, and has now reached the point at which all support vectors must be considered, the original decision function is applied. Satisfying this final rule, and all intermediary rules, is the only way for a test image to garner a positive (face) classification.

An entrance guard access method based on a two-dimension code and face identification comprises the steps that a resident applies to a background server for the visitor gate pass two-dimension code through an entrance guard APP intelligent terminal; a visitor acquires the visitor gate pass two-dimension code from the background sever through the entrance guard APP intelligent terminal, and uploads a face image to the server to be related with a visitor ID; an entrance guard controller reads the visitor gate pass two-dimension code displayed on the entrance guard APP intelligent terminal through an input device, and takes the picture of the face image of the visitor; the entrance guard controller judges the consistency of generated decoding information and stored authentication information.Two-dimension code appointment and face identification are combined, so that the uniqueness and safety of appointment verification are guaranteed; furthermore, the whole identification process is achieved through an entrance guard identifier, cost is saved, and more importantly, time is saved, efficiency is improved, and using is quite convenient.

The invention discloses a face identification method and apparatus. The method includes the steps of extracting face characteristic vectors of a face image to be identified by using a convolution neural network model. The face characteristic vectors comprise image characteristics and facial attribute characteristics. Matching degree between the face image to be identified and face images in a face template database is calculated according to the face characteristic vectors, and the identification result of the face image to be identified is determined according to the matching degree. During a face identification process, the face identification accuracy is effectively improved based on comprehensive comparison of the face image characteristics and facial attribute characteristics.

The invention provides a crime monitoring method based on the face recognition technology and behavior and sound recognition, which includes the following steps: Step 1, recoding a video through a camera, reducing dimensions of the video to form a picture message ensemble; Step 2, performing the recognition comparison to the picture message ensemble as per an intelligent behavior pattern, and issuing an early warning and storing the video if the comparison is successful; and Step 3, verifying the crime situation by a police on duty, confirming the position of the camera through a GPS for positioning and tracking and confirming the police strength nearby, and sending crime situation to polices nearby by the police on duty, and if no police is on duty, confirming the position of the camera automatically through the GPS for positioning and tracking and confirming the police strength nearby, and sending crime situation to polices nearby and staff on duty. According to the invention, different intelligent behavior patterns are set as per the monitoring requirements of different situations, targeted monitoring is introduced, early warning prevention in advance is realized, the case is prevented from further worsening, the time in solving a criminal case is shortened, and the detection rate is improved.

Face representation is a crucial step of face recognition systems. An optimal face representation should be discriminative, robust, compact, and very easy to implement. While numerous hand-crafted and learning-based representations have been proposed, considerable room for improvement is still present. A very easy-to-implement deep learning framework for face representation is presented. The framework bases on pyramid convolutional neural network (CNN). The pyramid CNN adopts a greedy-filter-and-down-sample operation, which enables the training procedure to be very fast and computation efficient. In addition, the structure of Pyramid CNN can naturally incorporate feature sharing across multi-scale face representations, increasing the discriminative ability of resulting representation.

The embodiment of the invention discloses self-adapting face identification method and device relating to the field of face identification. The self-adapting face identification method comprises the following steps of: typing in the basic information of a face user; acquiring a plurality of face images of the face user and respectively extracting the face features from the face images to serve as a face feature template; saving the base information of the face user and the face feature template to a face user information base; extracting the face features from the acquired face images, and carrying out comparison identification with the face feature template in the face user information base; if the identification passes, further judging whether the face features meet the preset self-adapting demands or not; and if yes, updating the face feature template in the face user information base to complete the face feature self adapting. The method effectively eliminates the influence generated on the face identification effect because the face features slowly change with the changes of ages, beards and the like, thereby improving the identification speed and accuracy.

The invention relates to a method and system for screening face images. The method comprises the following steps: according to preset face integrity standards, face size threshold and face position threshold, obtaining a first screening result; according to a preset definition threshold, obtaining a second screening result; according to a preset best angle threshold, obtaining a third screening result; and according to the sum of similarities between each face image and other face images, the definition of each face image and the face deflection angle, obtaining the weighting sum of each face image in the third screening result, and acquiring a face image with the maximum weighting sum for face identification. The method can be used for solving the problem that the face is mistakenly identified due to the low quality of an input image, the data volume of the face images is effectively reduced, and the operation load during face identification is relieved; and moreover, the data volume of the face images is reduced, front face images with high quality are effectively screened to be identified, and the accuracy and real-time performance of face identification are improved.

The invention relates to a social insurance identity authentication method based on face recognition and living body detection, which comprises implementation steps as follows: a face template base database is established and a social insurance user logs on to an identity authentication module which completes the combination of face recognition and living body detection,, a face template online update module, an audit query module, a complaint processing module and a multiple auditing module. The authentication method runs in the following way that: a user logins to generate a face template of the user, the identity authentication module which combines face recognition and living body detection is used to perform identity authentication of face video streams of the user displayed in a front-end video device; if authentication succeeds, auditing is performed while updating the template, and social insurance fund is issued; if authentication fails, a complaint program is used for multiple auditing, and if auditing by all staff passes, the social insurance can be issued. The method can improve the service quality and working efficiency of social insurance processing and can effectively restrain the loss of the social insurance fund.

The invention discloses a face-recognition-based network video monitoring device and a monitoring recognition method. The face-recognition-based network video monitoring device comprises a central processing unit, a audio / video signal processing module, a storage module, a clock module, a power supply module, an alarm module, an acquisition photographing module, a snap-shot photographing module, a network communication module and an infrared light source module. The acquisition photographing module is used for acquiring video data to obtain real-time video monitored images and detect a face image target in accordance with a snap-shot requirement. The snap-shot photographing module is used for performs snap-shot on the target, which is detected by the acquisition photographing module and is in accordance with the snap-shot requirement, to obtain a face image of the target and transmit the face image for carrying out targeted postprocessing. The network communication module is used for transmitting the acquired video data and the snap-shot face image to a network. The monitoring recognition method comprises the steps of: carrying out real-time figure comparison on the face image and a face image database of the storage module, or transmitting the face image to a client side to carry out figure search comparison on the face image and the face database of a figure server, and feeding back a comparison result in an alarm manner. According to the invention, the application occasions of video monitoring equipment are expanded, and the security of the application occasions is increased.

The invention discloses a face recognition method and system, in particular relates to a face recognition model training method and system. The face recognition method comprises the following steps: every user for recognition is trained and a face recognition model is obtained; within the set face posture range, the face image with structure light coding and the face image without structure light coding of the present user waiting for recognition are collected simultaneously; a face range image is obtained based on the face image with structure light coding, and a face gray image is obtained based on the face image without structure light coding; the face gray image is used for calibration, and face pre-treatment is conducted for the face gray image and the face range image based on the calibration results; the characteristics of pretreated face range image and face gray image are calculated respectively; the characteristics are input to the face recognition model to recognize faces and output face recognition results. The technical proposal provided by the invention can reduce the interference of light to face recognition and reduce costs simultaneously.

The invention discloses a multi-pose eye positioning algorithm based on the cascaded convolutional neural network, belongs to the machine learning and computer vision field and is suitable for intelligent systems such as face recognition, sight tracking and driver fatigue detection. The method comprises steps that face pictures marked with various types of information are collected to form a training data set; the multi-task cascaded convolutional neural network is constructed; the training data set is utilized to train the network to acquire a network model; and lastly, the network model is utilized to detect faces and face key points of the pictures, and the smallest rectangular box containing the eye key points is selected as the eye positioning result. The method is advantaged in thatthe multi-task cascading convolutional neural network is utilized to accomplish face detection and face key point detection, so the multi-pose eye positioning effect is obviously improved.

The invention relates to the field of intelligent advertisement putting and data analysis, in particular to a method and system for analyzing big data of intelligent advertisements based on face identification. The method comprises the following steps: correlatively presetting, storing face image information in a local database, and distributing corresponding user identifiers; starting application, and displaying a face identification detection interface of which at least one part is used for displaying advertisement information; performing tracking identification on faces, and extracting the characteristic values of the faces; setting the threshold values of images and judging whether the threshold values of the images are matched or not; calling related data of the identifiers and pushing personalized media information; recording advertisement watching time, clicking, residence time and identified facial expressions, and uploading to a server; clustering and analyzing acquired multi-user information to obtain statistic data; adjusting pushing of advertisement information according to the statistic data. According to the method and the system, an advertisement pushing window is added to existing face detection equipment, and consumption habits or behaviors of groups can be obtained according to data analysis, thereby providing guidance for commercial activities.

A face descriptor generating method and apparatus and face recognition method and apparatus using extended local binary pattern (LBP) are provided.Since LBP features are selected by performing a supervised learning process on the extended LBP features and the selected extended LBP features are used in face recognition, it is possible to reduce errors in face recognition or identity verification and to increase face recognition efficiency. In addition, the extended LBP features are used so that it is possible to overcome the problem of time-consumption of the process.

A method and system for identifying a human being or verifying two human beings by partial component(s) of their face which may include one or multiple of left eye(s), right eye(s), nose(s), mouth(s), left ear(s), or right ear(s). A gallery database for face recognition is constructed from a plurality of human face images by detecting and segmenting a plurality of partial face components from each of the human face images, creating a template for each of the plurality of partial face components, and storing the templates in the gallery database. Templates from a plurality of partial face components from a same human face image are linked with one ID in the gallery database. A probe human face image is identified from the gallery database by detecting and segmenting a plurality of partial face components from the probe human face image; creating a probe template for each of the partial face components from the probe human face image, comparing each of the probe templates against a category of templates in the gallery database to generate similarity scores between the probe templates and templates in the gallery database; generating a plurality of sub-lists of candidate images having partial face component templates with the highest similarity scores over a first preset threshold; generating for each candidate image from each sub-list a combined similarity score; and generating a final list of candidates from said candidates of combined similarity scores over a second preset threshold.

An in vivo face detection method based on a convolutional neural network relates to the machine learning and mode identification field, and is provided to face a cheat problem in the face identification. A conventional face identification technology is very easy to attack, the attackers frequently copy the faces of the legal users by the methods, such as the photographs, the videos, the 3D models, etc., if a face identification system can not distinguish the real faces and the fake faces effectively, the invaders can pass the face identification system very easily by the fake identities. Based on the problem, the present invention provides the in vivo face detection method based on the convolutional neural network. The convolutional neural network in the method provided by the present invention is realized based on a cuda_convnet framework, the network structure comprises four convolutional layers of two max-pooling layers, one full connection layer and one soft_max layer, and the soft_max layer comprises two nerve cells used for predicting the probability distribution of the real and fake faces.

The invention discloses a skin type and skin problem identification and detection method based on facial image identification. The method comprises the steps that a clear face picture is input, face identification is conducted, and a face picture image of a human face is divided into twenty face image blocks; hair and skin identification is conducted on each divided face image block, and skin properties of each divided face image block are calculated; skin problems in each face image block are identified; according to results of the skin problems and the skin properties, the skin type is judged, and the skin type and the skin problems are identified and detected. According to the method, on the basis of one clean face picture, face identification, regional division and regional identification and other methods are adopted in sequence, the skin type and the skin problems of the human face in the picture in all aspects are determined, and therefore the most suitable skin care suggestion can be provided through detection on the face in the image.