5842results about How to "Improve recognition rate" patented technology

The invention discloses a display device. The device comprises a display panel with a plurality of pixel units, a protective cover plate arranged on a light-out surface of the display panel, a plurality of photosensitive devices arranged at one side of the pixel unit opposite to the light-out surface and used for line recognition, an optical collimator arranged between the protective cover plate and the plurality of photosensitive devices is added, the optical collimator is provided with a transmittance area on the top of the photosensitive devices, the remaining area except for the transmittance area is a shading area, part of stray light adjacent to a ridge and display luminescence given out by the pixel units and entering the photosensitive devices can be filtered by the shading area, the transmittance area can ensure that the ridge reflected light enters the corresponding photosensitive devices, the line recognition rate of the photosensitive devices is improved, and therefore the precision and definition of line recognition images are improved.

A system and method for correcting data in data fields of financial documents containing unreadable characters is described.

The invention discloses a train operation fault automatic detection system and method based on binocular stereoscopic vision, and the method comprises the steps: collecting left and right camera images of different parts of a train based on a binocular stereoscopic vision sensor; achieving the synchronous precise positioning of various types of target regions where faults are liable to happen based on the deep learning theory of a multi-layer convolution neural network or a conventional machine learning method through combining with the left and right image consistency fault (no-fault) constraint of the same part; carrying out the preliminary fault classification and recognition of a positioning region; achieving the synchronous precise positioning of multiple parts in a non-fault region through combining with the priori information of the number of parts in the target regions; carrying out the feature point matching of the left and right images of the same part through employing the technology of binocular stereoscopic vision, achieving the three-dimensional reconstruction, calculating a key size, and carrying out the quantitative description of fine faults and gradually changing hidden faults, such as loosening or playing. The method achieves the synchronous precise detection of the deformation, displacement and falling faults of all big parts of the train, or carries out the three-dimensional quantitative description of the fine and gradually changing hidden troubles, and is more complete, timely and accurate.

The present invention discloses a generative adversarial network-based multi-pose face generation method. According to the generative adversarial network-based multi-pose face generation method, in a training phase, the face data of various poses are collected; two deep neural networks G and D are trained on the basis of a generative adversarial network; and after training is completed, the generative network G is inputted on the basis of random sampling and pose control parameters, so that face images of various poses can be obtained. With the method of the invention adopted, a large quantity of different face images of a plurality of poses can be generated, and the problem of data shortage in the multi-pose face recognition field can be solved; the newly generated face images of various poses are adopted as training data to train an encoder for extracting the identity information of the images; in a final testing process, an image of a random pose is inputted, and identity information features are obtained through the trained encoder; and the face images of various poses of the same person are obtained through the trained generative network.

The embodiment of the invention provides a face recognition method and device. The face recognition method comprises the following steps: detecting whether a video streaming image contains the information of the local characteristics of a face, determining the face contained in the video streaming image according to a detection result, determining the face which is effectively recognized, and carrying out living body detection recognition; when the face which is effectively recognized meets a living body detection recognition condition, extracting a single frame of image or picture of the video streaming image, and generating an individual characteristic head portrait; according to the individual characteristic head portrait, extracting individual characteristic information; comparing a similarity between the individual characteristic information with sample plate characteristic information in an individual characteristic library; and starting a relevant application program when the similarity is greater than a preset similarity threshold value. Correspondingly, the embodiment of the invention also provides a face recognition device. According to the technical scheme provided by the embodiment of the invention, influence on a face recognition operation by external environment can be favorably eliminated, accuracy is higher when the identity of a user is judged through the face, and a recognition rate is improved.

The invention discloses a system and a method for smiling face recognition in a video sequence. The system comprises a pre-processing module, a feature extraction module, and a classification recognition module. According to the pre-processing module, through video collection, face detection and mouth detection, a face image region capable of directly extracting optical flow features or PHOG features can be acquired; according to the feature extraction module, Optical-PHOG algorithm is adopted to extract smiling face features, and information most facilitating smiling face recognition is obtained; and according to the classification recognition module, random forest algorithm is adopted, and classification standards on a smiling face type and a non-smiling face type are obtained according to feature vectors of a large number of training samples obtained by the feature extraction module in a machine learning method. Comparison or matching or other operation is carried out between feature vectors of a to-be-recognized image and the classifier, and the smiling face type or the non-smiling face type to which the to-be-recognized image belongs can be recognized, and the purpose of classification recognition can be achieved. Thus, according to the system and the method for smiling face recognition in the video sequence, accuracy of smiling face recognition can be improved.

The invention relates to a face feature extraction method based on a face feature point shape drive depth model. The method comprises the steps that the face feature point shape drive depth model is set up, N depth convolution neural networks are utilized for extracting features of N face regions divided according to the positions of face feature points to obtain the discrimination feature and the attributive feature of each region, and then all the discrimination features and the attributive features are fused to obtain features higher in descriptive ability. According to the face feature extraction method based on the face feature point shape drive depth model, the problem of robustness under change conditions of illumination, angles, expressions, shielding and the like can be well solved, and the recognition rate of face recognition under these conditions is increased.