A discriminant sparse preserving embedding method for unconstrained face recognition

Abstract

The invention provides a discriminant sparse preserving embedding method for unconstrained face recognition, 1) calculating a sample reconstruction relation matrix W, when calculating the sparse reconstructed relation of samples, the class label is introduced to construct the intra-class reconstructed relation matrix and inter-class reconstructed relation matrix respectively, and the intra-class and inter-class compactness constraint is added in the sparse reconstructed stage, which effectively increases the reconstructed relation between the samples to be tested and the same kind of samples,and weakens the reconstructed relation between the samples to be tested and the heterogeneous samples. 2) When calculating the low-dimensional projection matrix P and the low-dimensional projection matrix, the global constraint factor is added, which not only considers the local sparse relation of the sample, but also considers the global distribution characteristic, further weakens the disturbance of the heterogeneous pseudo-nearest neighbor sample to the low-dimensional projection, and more accurately excavates the essential structure of the low-dimensional manifold hidden in the complex redundant data; 3) The low-dimensional linear mapping of high-dimensional sample data is realized, which greatly improves the accuracy of face recognition in unconstrained environment.

Description

technical field [0001] The invention relates to a discriminant sparse-preserving embedding method for unconstrained face recognition, which uses face recognition in an unconstrained environment as the application background to conduct low-dimensional mapping research on high-dimensional face data, mainly including sample sparse reconstruction Relational Optimization and Improvement of Low-Dimensional Projection Objective Functions. Background technique [0002] With the rapid development of the Internet and sensor technology, the facial image data processed by computers is becoming more and more massive and complex. Therefore, it is particularly important to effectively reduce the dimensionality of massive complex face data and dig out the useful essential information hidden under the high-dimensional appearance. On the one hand, it can reduce the data dimension, save storage space, and improve the operating efficiency of the system; on the other hand, it can obtain the ess...

AI Technical Summary

Problems solved by technology

[0009] 1. LPP, NPE and their improved algorithms are linear implementations of traditional manifold learning algorithms, which effectively maintain the local neighbor relationship between samples, but in high-dimensional space, describing the neighbor relationship with the Euclidean distance between samples does not have Distinguishability, therefore, expressing the neighbor relationship with the distance measure cannot accurately mine the useful low-dimensional essential information hidden in the high-dimensional redundant data

[0010] 2. In the locally preserving dimensionality reduction algorithms represented by LPP and NPE, the construction of the nearest neighbor graph plays a crucial core role, but the selection of appropriate graph parameters is still a technical difficulty for this type of algorithm. The tiny neighbor graph Any parameter change will lead to a sharp decline in the performance of the algorithm.

However, when dealing with face data acquired in an unconstrained environment, the samples are complex and changeable. Traditional SPP is an unsupervised dimensionality reduction method, and the optimized sparse reconstruction weights cannot accurately reflect the identification information. Moreover, in low-dimensional When projecting, the global distribution characteristics of the samples are not considered, which affects the accuracy of unconstrained face recognition

Images