Face recognition method and system

Abstract

The invention discloses a face recognition method and system. The method comprises the steps of: extracting feature vectors of face images with labels and face images without labels to obtain a firstfeature vector set and a second feature vector set; calculating soft label information of the label-free face image; calculating inter-class divergence and intra-class divergence of the face image training set and executing a linear discriminant analysis algorithm to obtain a discriminant projection matrix; performing dimension reduction on the feature vector of the to-be-detected face image, thefirst feature vector set and the second feature vector set by utilizing the identification projection matrix to obtain respective low-dimensional feature vectors, and inputting the low-dimensional feature vectors into a collaborative representation classifier to obtain respective collaborative representation codes, and calculating a reconstruction residual error by utilizing the collaborative representation code corresponding to each type, wherein the type of label with the minimum reconstruction residual error is a to-be-detected sample label. According to the method, the label information with the label data is transmitted to the data without the label, the number of the training samples with the label is increased, all the samples are identified and analyzed, and the CRC precision and discriminability are improved.

Description

technical field [0001] The invention belongs to the field of face recognition, and in particular relates to a face recognition method and system. Background technique [0002] Face recognition is one of the most important applications in the field of pattern recognition technology. The so-called face recognition is the use of computers to analyze face videos or images, and extract effective personal identification information from them, and finally determine the identity of the face object. [0003] Collaborative Representation Classification (CRC) is an efficient and high-speed classifier, which has been widely used in the field of image recognition. The basic idea of ​​CRC is to use all training samples to jointly represent the test sample, and then obtain a cooperative representation code. Then use a class code selector to select the class code corresponding to a certain class of training samples, and finally use the class code to calculate the reconstruction residual o...

AI Technical Summary

Problems solved by technology

[0004] CRC is essentially a supervised learning method, and its performance depends heavily on the number of labeled training samples of each type. When the number of labeled training samples of each type is not sufficient, the recognition performance of CRC will be significantly reduced.

[0005] Secondly, although CRC has a good representation ability, its ability to distinguish different types of samples is weak. In order to increase the robustness of the model and make the face images of different people distinguish as much as possible, it is necessary to increase the discrimination ability of the CRC model itself.

Images