Semi-supervised face identification method and system based on fuzzy membership degree sparse reconstruction

Abstract

The invention discloses a semi-supervised face identification method and system based on fuzzy membership degree sparse reconstruction. The identification method comprises steps of obtaining a face image data set which comprises a training sample subset and a test sample subset; according to known category labels of training samples, obtaining a fuzzy membership degree initialization matrix of the training sample subset; according to residual errors of test samples about the category labels, obtaining a fuzzy membership degree initialization matrix of the test sample subset; obtaining a fuzzy membership degree initialization matrix of the face image data set about the category labels further; solving sparse coefficients of all test samples, and obtaining a sparse solution matrix of the test sample subset; according to the fuzzy membership degree initialization matrixes and the sparse solution matrix of the test sample subset, iteratively solving a fuzzy membership degree matrix of the updated face image data set about the category labels; and obtaining a category label corresponding to the maximal membership degree of each test sample to complete classification.

Description

technical field [0001] The invention belongs to the field of pattern recognition, in particular to a semi-supervised face recognition method and system based on sparse reconstruction of fuzzy membership degrees. Background technique [0002] With the rapid development of computer technology and image processing technology, face recognition has attracted the attention of many researchers due to its wide application, and has become an important aspect of modern pattern recognition technology research. [0003] However, labeling face images is time-consuming and labor-intensive. Considering the simple and easy-to-obtain unlabeled face images, we can make full use of the label information in the labeled face images and the feature information in the unlabeled face images to improve the unlabeled face images. Classification, this learning scheme is called semi-supervised learning. [0004] Among them, graph-based semi-supervised learning is one type of learning scheme. Graph-bas...

AI Technical Summary

Problems solved by technology

[0007] The above methods can fully obtain the local and global features between the data, but the sparse reconstruction residual of the data is ignored, and the residual of the data is the key factor of semi-supervised learning based on sparse representation, so Will reduce the classification recognition accuracy of semi-supervised methods

Images