Unconstrained face recognition method based on weighted tensor sparse graph mapping

Abstract

The invention discloses an unconstrained face recognition method based on weighted tensor sparse graph mapping, and relates to the technical field of face recognition methods. In the sparse graph construction stage, training samples (images) are represented by second-order tensors, a supervised over-complete tensor dictionary is constructed, and similar sparse reconstruction coefficients of the samples are optimized and solved; and a more accurate tensor sparse neighbor graph is constructed in a self-adaptive manner. In a bilateral low-dimensional projection stage, low-dimensional tensor subspace distribution is obtained by utilizing identification information implied in sample global distribution. And low-dimensional mapping yWTSGE = UTyV is performed on the to-be-tested sample y by adopting the optimal WTSGE bilateral projection matrixes U and V, and a classifier is trained by using a low-dimensional training sample DWTSGE = UTXV to realize accurate identity authentication of the non-constrained face. According to the method, the complexity of the non-constrained face image data is fully considered, the neighbor distribution diagram of the high-dimensional tensor data is obtainedin a self-adaptive mode through the sparse representation technology, the low-dimensional manifold essential structure of the highly-distorted non-constrained face data is effectively extracted, andthe accuracy of non-constrained face recognition is greatly improved.

Description

technical field [0001] The present invention relates to the technical field of face recognition methods, in particular to the technical field of unconstrained face recognition methods based on weighted tensor sparse graph mapping. Background technique [0002] With the rapid development of mobile Internet, electronic sensing technology, and machine learning theory, real-time collection of face images for identity authentication, video surveillance and human-computer interaction has become an important application of artificial intelligence in actual work and life. Due to the mixed interference of various factors such as illumination, posture, expression, occlusion, age, and resolution, the face data collected in the real environment leads to the diversity of face images and a highly complex nonlinear distribution in high-dimensional space. . Therefore, how to effectively reduce the dimensionality of high-dimensional massive unconstrained face data is particularly important....

AI Technical Summary

Problems solved by technology

When solving the low-dimensional projection matrix in this space, it is necessary to calculate the eigenvalues ​​of the high-dimensional matrix, which leads to an increase in the computational complexity of the algorithm and the disaster of dimensionality.

[0007] (3) The number of samples in a high-dimensional vector space is often smaller than the space dimension, and singular value problems will appear when solving low-dimensional projection matrices

To solve this problem, the PCA method is usually used to reduce the dimension of the vector space, which will lose some useful discriminant information of the data to a certain extent.

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