40results about How to "Accurate face recognition" patented technology

The invention discloses a method and system of getting human face images, which use initiative lamp-house to irradiate the token area of human face; during identifying the faces, using electron image collection means used in the initiative lamp house to take photos in order to get corresponding images, and further, transmitting the said images to corresponding electron handle device to take the process of identifying the human faces. The invention can efficiently reduce that the change of light influence on the human face images in different condition of irradiation. On using, using initiative lamp house to irradiate the human faces, so as to preciously identify the faces in various irradiation conditions. In the imaging of the human faces, as the influence of beam intensity of initiative lamp house is stronger than beam intensity of environment lamp-house, hence collected faces' images is most stable, and getting the best results of computer identifying.

The invention discloses a depression angle face recognition method and system based on an axial attention weight distribution network. First, CNN is used to extract features from face image sequences of different angles and resolutions to generate a set of normalized feature vectors; Secondly, the cascaded axial attention weight assignment module is used to adaptively assign weights to the image feature sequence, and obtain the weight vector (horizontal) of each feature map itself and the weight vector (vertical) between features to form an axial weight matrix; The weighted aggregation is carried out by using the axial weight to obtain a more discriminative feature vector for identification. The axial matrix weight of the present invention can more delicately represent the weight distribution result between the face sequence images, so the fused features have stronger discrimination, which is beneficial to more accurate depression angle face recognition.

The invention discloses a face recognition and analysis method used in monitoring video data, which uses two neural networks to compare and score the high-latitude face features of the face, and integrates the K-adjacent algorithm and the screening mechanism with the Euclidean distance as the threshold to achieve Face comparison and recognition, so as to obtain accurate face recognition results; in addition, the present invention also performs further screening according to the time of video data and the frequency of appearance of faces, finally obtains the recognition results, and then obtains more accurate face recognition result. The invention can perform relatively accurate face recognition on video data or picture data captured by real-time or historical monitoring cameras.

The invention discloses a method and system for determining social intimacy. The method includes: acquiring an image collected by a camera; using a cascaded neural network to detect face information in the image; using a convolutional neural network to determine a sparse representation of the detected face information; using the sparse representation of the face information as an input , use the classifier to classify the faces, and get the faces representing different actors; use the faces of different actors as targets to track, and obtain the behavior information of each actor, the behavior information includes the behavior between the actors Distance information and conversation behavior between actors; determine the intimacy between actors according to the behavior information of each actor. The method and system for determining social intimacy provided by the present invention can automatically acquire the intimacy between actors based on camera monitoring.

The invention discloses a human face recognition method based on the bi-directionally and two-dimensionally iterative and non-relevant discriminant analysis. Firstly, the image sample of a human face is acquired. After that, within-class scatter matrixes, inter-class scatter matrixes and overall scatter matrixes in the horizontal and vertical directions are respectively calculated. By utilizing the non-relevant discriminant transformation method, a first optimal differential vector and an optimal discriminant vector set in each direction are respectively calculated. Afterwards, the projection and dimensionality reduction of the image is conduced by the two optimal discriminant vector sets at the same time. Finally, the classified calculation is conducted by a nearest neighbor classifier to figure out the recognition rate of the image. According to the technical scheme of the invention, the non-relevant discriminant transformation on two-dimensional images is conducted based on the discrimination information in two directions. In this way, the dimensionality reduction and the discrimination information extraction of two-dimensional images both in the horizontal direction and in the vertical direction can be realized at the same time. Therefore, the correct facial recognition is realized. Both the processing time and the storage space are saved, and the recognition rate is ensured to be high.

The invention discloses a face recognition method, device, device and storage medium based on radar technology. The method detects the face through two wireless detection modules and generates two corresponding depth maps, and according to the two depth maps Get the shooting angle of the camera to the face; according to the shooting angle, get the corresponding feature distance ratio correction coefficient, and calibrate the face recognition process of the face image captured by the camera according to the feature distance ratio correction coefficient. The present invention can judge whether the face to be recognized is a real face, can effectively avoid the phenomenon of using an image face to pretend to be a real face for face recognition, and can improve the safety and accuracy of face recognition.