Double linear regression classification for face recognition

Abstract

A new classifier designed based on linear regression classification (LRC) classifier and simple-fast representation-based classifier (SFR), named double linear regression classification (DLRC) classifier, is proposed for image recognition in this paper. As we all know, the traditional LRC classifier only uses the distance between test image vectors and predicted image vectors of the class subspace for classification. And the SFR classifier uses the test image vectors and the nearest image vectors of the class subspace to classify the test sample. However, the DLRC classifier computes out the predicted image vectors of each class subspace and uses all the predicted vectors to construct a novel robust global space. Then, the DLRC utilizes the novel global space to get the novel predicted vectors of each class for classification. A mass number of experiments on AR face database, JAFFE face database, Yale face database, Extended YaleB face database, and PIE face database are used to evaluate the performance of the proposed classifier. The experimental results show that the proposed classifier achieves better recognition rate than the LRC classifier, SFR classifier, and several other classifiers.

Keywords:

• face image representation and recognition
• linear regression classification
• nearest neighbor classifier
• simple-fast representation-based classifier

Additional information

Funding

Funding. This work was supported by Peacock Project of Shenzhen, Intelligent Vehicle Cloud Client System [grant number KQC201109020055A]; National Nature Science Foundation of China [grant number 61375021], [grant number 61472138], [grant number 61263032]; Natural Science Foundation of Jiangsu Province [grant number SBK201322136].