Face recognition method based on supervisory neighbour keeping inlaying and supporting vector machine

Abstract

The invention discloses a facial recognition method based on supervised neighborhood preserving embedding (SNPE) and a support vector machine (SVM). The method comprises a training process and a test process, and includes specifically the following procedures: a, a weight matrix for a given data sample group is constructed; b, according to the weight matrix obtained in the procedure a, a generalized characteristic vector problem about the data samples is solved and an embedding matrix projecting the data samples into a low-dimensional data space is searched; c, characteristic extraction is performed on the data samples with the embedding matrix to acquire characteristic data of the low-dimensional space; d, mode classification of the acquired characteristic data in the procedure c is conducted with the SVM to realize the type recognition of the data samples. The facial recognition method provided by the invention can solve the problems in the prior that the linear dimensionality reduction method cannot maintain well inter- and intra-type sample structures; the non-linear dimensionality reduction method has large computational amount; and the common classifier has over-learning and under-learning problems.

Description

technical field [0001] The invention relates to a face recognition method, in particular to a face recognition method based on Supervised Neighbor Preserving Embedding (SNPE) and Support Vector Machine (SVM), and belongs to the technical field of image processing and pattern recognition. Background technique [0002] Face recognition is a pattern recognition problem, and feature extraction is an important link that all classification systems in pattern recognition need to solve. dimensionality to reduce the complexity of the classification system, which involves the so-called manifold learning problem. The classic techniques in manifold learning are linear dimensionality reduction methods. For example, principal component analysis (PCA) can achieve linear or nearly linear embedding of manifolds; when there is available class information, linear discriminant analysis (LDA) can Find an optimal linear subspace for classification. However, these linear dimensionality reduction...

AI Technical Summary

Problems solved by technology

[0005] The present invention provides a face recognition method based on supervised neighbor-preserving embedding and support vector machine, which can solve the problem that the linear dimensionality reduction method cannot well maintain the sample structure within and between classes, and the calculation amount of the nonlinear dimensionality reduction method Over-learning and under-learning problems in large and common classifiers

Images