355 results about "Face verification" patented technology

A system and a method for rapidly tracking multiple faces uses a face-like region generator to find a face-like region by skin color, motion, and silhouette information. A face tracking engine tracks faces based on new and old faces, and skin colors provided by the face-like regions. The tracked face is fed into a face status checker for determining whether the face-like regions are old faces tracked in a previous frame or are possible new faces. If the face-like regions are old faces, a face verification engine checks whether there exists a predefined percentage of overlapping area between an old face and a skin region. If yes, the old face is still in the current frame and its position is in the center of the skin region, otherwise, the position of the old face is found by a correlation operation.

A face imaging system for recordal and/or automated identity confirmation, including a camera unit and a camera unit controller. The camera unit includes a video camera, a rotatable mirror system for directing images of the security area into the video camera, and a ranging unit for detecting the presence of a target and for providing target range data, comprising distance, angle and width information, to the camera unit controller. The camera unit controller includes software for detecting face images of the target, tracking of detected face images, and capture of high quality face images. A communication system is provided for sending the captured face images to an external controller for face verification, face recognition and database searching. Face detection and face tracking is performed using the combination of video images and range data and the captured face images are recorded and/or made available for face recognition and searching.

A system for providing stand-off biometric verification of a driver of a vehicle at a control gate while the vehicle is moving, including an RFID vehicle tag reader, an RFID personal tag reader and a facial detection and recognition (verification) system. The RFID vehicle tag reader scans and reads data from an RFID vehicle tag of the vehicle that is trying to pass through the gate. The RFID personal tag reader reads data from an RFID personal tag carried by personnel who are driving in the vehicle. The facial detection and verification system scans and reads facial images for the driver. All the data and facial images detected by the reader are sent to a computer for further processing (final face verification).

A system for providing stand-off biometric verification of a driver of a vehicle while the vehicle is moving and/or a person on foot at a control gate, including an RFID vehicle tag reader, an RFID personal smart card reader and a facial detection and recognition (verification) system. The driver carries a RFID personal smart card that stores personal information of the driver and a face template of the driver. The vehicle carries a RFID vehicle tag that stores information regarding the vehicle. When the vehicle approaches the control gate, the RFID vehicle tag reader reads data from the RFID vehicle tag and the RFID personal tag reader reads data from the RFID personal smart card. The facial detection and verification system scans and reads a facial image for the driver. All the data and facial images detected by the readers are sent to a local computer at the control gate for further processing (final face verification). The local computer at the control gate decodes and retrieves the face template from the data read from the RFID personal smart card.

A system for providing stand-off biometric verification of a driver of a vehicle while the vehicle is moving and/or a person on foot at a control gate, including an RFID vehicle tag reader, an RFID personal smart card reader and a facial detection and recognition (verification) system. The driver carries a RFID personal smart card that stores personal information of the driver and a face template of the driver. The vehicle carries a RFID vehicle tag that stores information regarding the vehicle. When the vehicle approaches the control gate, the RFID vehicle tag reader reads data from the RFID vehicle tag and the RFID personal tag reader reads data from the RFID personal smart card. The facial detection and verification system scans and reads a facial image for the driver. All the data and facial images detected by the readers are sent to a local computer at the control gate for further processing (final face verification). The local computer at the control gate decodes and retrieves the face template from the data read from the RFID personal smart card.

To reduce degradation of recognition performance due to eye detection errors during face verification and to overcome a problem in that sufficient data to design an optimum feature classifier cannot be obtained during face registration, a method includes shifting the positions of eyes detected during face registration in predetermined directions by a predetermined distance to generate pairs of new coordinate points of the eyes; normalizing a face image on the basis of each pair of new coordinate points of the eyes; using the results of normalization in teaching a feature classifier, thereby coping with eye detection errors. In addition, two threshold values are used to prevent a database from being updated with a face of an unregistered person and to update the database with a normal client's face image that has been used during the latest face verification.

A method of face recognition, and a system to perform the method, the method including performing face identification by comparing a face image received from a user with stored face information; performing face verification by comparing the received face image and the stored face information which corresponds to personal information received from the user; and updating the stored face information according to the received face image in response to authenticating the user through a successful face verification.

The invention discloses a face image super-resolution reconstruction method based on a discriminable attribute constraint generative adversarial network, and belongs to the field of digital images/video signal processing. The method comprises the following steps: firstly, designing a processing flow of face detailed information enhancement; secondly, designing a network structure according to theflow, and acquiring an HR image from an LR image through the network; and lastly, performing face verification accuracy evaluation on the HR image through a face recognition network. Through adoptionof the method, enhancement including LR face image detailed information can be completed, and the accuracy of face verification is increased. Secondly, the generative network completes compensation ofimage high-frequency information firstly, then completes image amplification by subpixel convolution, and finally completes stepwise image amplification through a cascade structure, thereby completing enhancement of image detailed information. An attribute constraint module are trained together with a perception module and an adversarial model in order to perform fine adjustment of the performance of a network reconstructed image. Finally, a reconstructed image of the generative network is input into a face verification network, so that the accuracy of face verification is increased.

The invention discloses a cross-age face verify method based on characteristic learning; the method comprises the following steps: 1, obtaining to-be compared two face images; 2, using a face characteristic point positioning method to carry out align operation for the two face images; 3, respectively carrying out feature extraction for each image, wherein the extraction method includes the following steps: a, automatically extracting high-level meaning characteristics through a depth convolution nerve network; b, calculating LBP histogram characteristics of the image; c, fusing the characteristics obtained in the a and b steps, and expressing characteristic vectors; 4, using a cosine similarity method to calculate a distance between the characteristic vectors obtained by the step3, and determining whether the two images are from a same person or not. The method firstly uses the depth network to cross-age face verify, and creatively combines the handwork design LBP histogram characteristics with depth network autonomous learning characteristics, thus realizing complementation between high-rise meaning characteristic and lower characteristics, and providing better accuracy.

The invention provides a deep learning face verification method based on mixed training. The method comprises the steps that a face data set is prepared; face and face key point detection is conducted on all images; all faces are normalized to obtain a face image training set, the face image training set is partitioned into a training data set and a verification data set, a mean image of all face images is calculated; the mean image is subtracted from all the face images to obtain a mean training data set and a mean verification data set; a deep convolutional neural network is trained; a corresponding triad is generated for each face image, and a triad training data set and a triad verification data set are formed; the deep convolutional neural network is trained again; face and face feature point detection is conducted on two given images to be verified, the mean image is subtracted from the images, the images are input into the deep convolutional neural network, a network feedforward operation is conducted, and features are extracted; according to a selected threshold value, when the distance between the extracted features of the two images is larger than the threshold value, it is judged that the faces in the two images belong to a same person, and otherwise, it is judged that the faces belong to different persons.

A methodology is described to reduce the complexity of filters for face recognition by reducing the memory requirement to, for example, 2 bits/pixel in the frequency domain. Reduced-complexity correlations are achieved by having quantized MACE, UMACE, OTSDF, UOTSDF, MACH, and other filters, in conjunction with a quantized Fourier transform of the input image. This reduces complexity in comparison to the advanced correlation filters using full-phase correlation. However, the verification performance of the reduced complexity filters is comparable to that of full-complexity filters. A special case of using 4-phases to represent both the filter and training/test images in the Fourier domain leads to further reductions in the computational formulations. This also enables the storage and synthesis of filters in limited-memory and limited-computational power platforms such as PDAs, cell phones, etc. An online training algorithm implemented on a face verification system is described for synthesizing correlation filters to handle pose/scale variations. A way to perform efficient face localization is also discussed. Because of the rules governing abstracts, this abstract should not be used to construe the claims.

Face verification is performed using video data. The two main modules are face image capturing and face verification. In face image capturing, good frontal face images are captured from input video data. A frontal face quality score discriminates between frontal and profile faces. In face verification, a local binary pattern histogram is selected as the facial feature descriptor for its high discriminative power and computational efficiency. Chi-Square (χ²) distance between LBP histograms from two face images are then calculated as a face dissimilarity measure. The decision whether or not two images belong to the same person is then made by comparing the corresponding distance with a pre-defined threshold. Given the fact that more than one face images can be captured per person from video data, several feature based and decision based aggregators are applied to combine pair-wise distances to further improve the verification performance.

An automated face recognition apparatus and method employing a programmed computer that computes a fixed dimensional numerical signature from either a single face image or a set/track of face images of a human subject. The numerical signature may be compared to a similar numerical signature derived from another image to acertain the identity of the person depicted in the compared images. The numerical signature is invariant to visual variations induced by pose, illumination, and face expression changes, which can subsequently be used for face verification, identification, and detection, using real-world photos and videos. The face recognition system utilizes a probabilistic elastic part model, and achieves accuracy on several real-world face recognition benchmark datasets.

The invention discloses a human face verification method based on bilinear united convolutional nerve network. The human face verification method comprises steps of 1) using a human face image which is prepared in advance to perform convolutional nerve network (CNN) training, 2) using the human face image which is in a training set to perform bilinear CNN fine tuning, 3) inputting a human face image to be verified, segmenting the two images, extracting united characteristics outputted by the bilinear CNN, and 4) making an obtained vector go through self-encoding network training to obtain a final verification result. The human face verification method is based on the bilinear CNN, replaces two repeated inputs of an original bilinear nerve network with different human face verification input images, and brings forward a human face verification description factor. The human face description factor has robustness to illumination, shielding and posture change. Furthermore, the characteristic extracted by the bilinear CNN has a smaller dimensionality than the characteristic dimensionality of a common CNN fully connected layer, which reduces number of parameters, makes follow-up deep belief network training simple and improves accuracy of human face verification.

The invention discloses an entrance guard control method based on face recognition. The entrance guard control method comprises the three steps of image information collecting, data processing and entrance guard operating, wherein core data processing comprises the steps that analysis is performed on the basis of returned video images, face images are automatically captured, compared and matched with existing photos in a database, and the faces are recognized. Particularly, means such as a face detection optimization data structure, picture preprocessing and feature extracting are integrated in data processing, and therefore high-speed high-accuracy face verification and recognition are achieved. According to the entrance guard control method, the defect that traditional face monitor and recognition are poor in light change, attitude angle and time span stability is overcome, the detection rate larger than 99% and the recognition rate larger than 80% within 5 meters are achieved, and the utilization rate of traditional hardware resources is increased and the application range of network monitor is widened except that intelligent control over entrance guard and attendance is effectively achieved.

The invention provides a design method of a safety face verification system based on a CNN (convolutional neural network) feature extractor, belongs to the field of biological feature identification, and particularly relates to a method of utilizing the CNN to extract face features and using a Paillier algorithm and an oblivious transfer technique to encrypt. Compared with the SCiFi (secure computation of face identification) system, the method has the advantages that the manually extracted feature is converted into the CNN self-learning feature, and the CNN self-learning feature is performed with binarization to remove the noise effect, so that the identification accuracy is higher; the testing identification rate is 91.48% on a view 2 of an LFW (labeled face wild) base; in the whole identification process, a server will not obtain any feature information of a requester, and only receive the feature ciphertext information, but not decrypt; a client only obtains whether the identification is passed or not, and does not know the other information, including hamming distance; one face picture is expressed by the 320bit feature, and compared with the SCiFi system, the feature data volume is decreased by 2/3, so that the consumption time of encrypting and identification is low, and the real-time performance is high.

The invention provides a face verification anti-counterfeit recognition method and a system thereof based on an interactive action. The method comprises a step of carrying out the initial recording of the information of an register static face image and the information multiple register face action images, a step of waiting the reading of a static face image to be read, matching a character and a stored character when the shooting of static face image to be detected obtained by a user to be verified is detected, and if a matching degree reaches the storage characteristic of a preset threshold value, conforming to a verification requirement, and a step of randomly selecting and prompting the user to be verified to complete a corresponding face action according to the recorded face action, extracting the characteristic of the action image to be detected of the user be verified, matching the characteristic with the historical verification feature information of a corresponding face action, completing face identity verification if a matching rate reaches a preset threshold value, adding the matched image into the historical verification feature information, returning to select a next face action to continue matching if the matching is not approved or does not reaches a desired effect, treating the verification as a failure if the action exceeds a preset number of times, and ending the verification.

A twin neural network training method for face verification comprises: a training sample set is prepared; the images in the training sample set are normalized in size and then input to the artificialneural network for training. Artificial neural network consists of two identical sub-neural networks. The training samples are divided into datasets data_p and data_p which are equal in number. The datasets data_p and data_p are sent to two sub-neural networks to extract the eigenvectors of the samples. By comparing the loss function to realize the iterative optimization of the neural network, until the iterative number reaches the set value, then jump out of the iteration, at this time the trained artificial neural network is the twin neural network for face verification; The contrast loss function represents similarity between two sets of eigenvectors.

The invention discloses a face authentication method based on a convolutional neural network and Bayesian decision. The face authentication method comprises the steps of 1), training the convolutional neural network and a Bayesian model by means of a face training database; 2), performing preprocessing such as face detection and face alignment on a testing database, and randomly combining test faces for obtaining 6000 pairs of faces; 3), extracting a characteristic vector of a testing face image pair by means of the convolutional neural network, and calculating similarity; and 4) after performing PCA dimension reduction on the characteristic vector, feeding the characteristic vector into a Bayesian network, calculating posterior probability according to the similarity, setting a threshold and determining whether each pair of faces belongs to one person. The face authentication method has advantages of improving robustness in face authentication and improving face authentication speed and face authentication accuracy. The face authentication method can be used in the field of identity authentication, public security, etc.

The invention discloses a face verification method and device. The method comprises: obtaining a textured face image and an initial face image needing verification; using a preset texture removing model to perform texture removing operation of the textured face image to obtain a de-textured face image; extracting features of the de-textured face image by means of a preset de-textured face verification model to obtain de-textured face features, and extracting features from the initial face image by means of the preset de-textured face verification model to obtain initial face features; and based on the de-textured face features and the initial face features, verifying the de-textured face image and the initial face image. According to the de-texture model and the de-textured face verification model trained in advance, the method conducts automatic processing and analysis of the textured face image and initial face image needing verification, with no need of manual operation by professionals. The method can automatically determine whether the textured face image and the initial face image are of the same person and improve the efficiency and accuracy of face verification.

The invention relates to an artificial intelligence convolutional neural network face recognition system. The system comprises a shooting terminal (100), a network (200), a server (300), a convolutional neural network module (400), an artificial intelligence early warning operation system (500), a cloud computing unit (600), a comparison analysis with a cloud database face blacklist unit (700), atarget person identity determination unit (800) and a local database module (900). The method comprises the following steps: acquiring an image or video stream containing a face by using a shooting terminal through an artificial intelligence convolutional neural network face recognition system, automatically detecting and tracking the face in the image, and further performing a series of technicalprocessing related to the face on the detected face, including face detection, face key frame extraction and face verification; all-weather 24-hour uninterrupted monitoring on the periphery of the shooting terminal is realized, a user can realize information sharing, the utilization rate of information resources is improved, and the safety guarantee is increased for maintaining the stability of social security.

A face feature is extracted by a face feature extracting unit, a confidence index is extracted by a confidence index extracting unit, and then, they are outputted as a face meta-data. At a time of matching, by using the confidence index of the face meta-data, a distribution estimating unit estimates the data of a parameter and the like with regard to a posterior distribution when the confidence index is obtained. A distance calculating unit calculates a similarity between feature values. Consequently, the precision of face verification can be improved, thereby carrying out a practical face matching.