Inter-class inner-class face change dictionary based single-sample face identification method

Abstract

The invention discloses an inter-class inner-class face change dictionary based single-sample face identification method to solve the problem of limitations of the current single-sample face identification algorithm. The method comprises the steps of step1, obtaining expressions of face images in the compression domain; step2, building a face image training sample matrix containing k classes; step3, building an average face matrix and an inter-class face change matrix of a face database; step4, adding low rank and sparse constraints into the inter-class face change matrix; step5, solving an inter-class similarity matrix and an inter-class difference matrix; step6, projecting the average face matrix, the inter-class similarity matrix and the inter-class difference matrix to low-dimensionality space; step7, performing normalization processing on the dimensionality reduced average face matrix, the inter-class similarity matrix and the inter-class difference matrix through a normalization method, and performing iterative solution on the face image training sample matrix based sparse coefficient vectors through a norm optimization algorithm; step8, selecting column vector face labels in the average face matrix, which are corresponding to the sparse coefficient maximum, to serve as the final face identification result.

Description

technical field [0001] The invention belongs to the technical field of computer vision and pattern recognition, and relates to a single-sample face recognition method based on an inter-class and intra-class face change dictionary. Background technique [0002] As an important biometric technology, face recognition has the advantages of naturalness and strong privacy. Therefore, it has attracted widespread attention in the past few decades, among which there are simpler but more successfully applied face recognition methods based on statistical features such as Eigenface, Fisherface, Laplacianfaces, etc. In recent years, sparse representation has been applied in the field of face recognition and has achieved great success. The face recognition classification method based on sparse expression (Sparse Representation Classifier, SRC), its idea is to use all training images as a super-complete dictionary, and the test image can be expressed as a linear combination of a few face ...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to overcome the defects of the prior art, solve the problem of the limitations of the current single-sample face recognition algorithm, and propose a single-sample face recognition method based on the face change dictionary between classes and within classes

Images