Multi-linear regression coefficient classifier for recognition

Qingxiang Feng, Qi Zhu, Chun Yuan, Ivan Lee

Abstract: In this paper, a new classifier, called multiple linear regression coefficients (MLRC), is proposed for image recognition. Linear regression classification (LRC) uses the linear combination of the class-model for classification. Sparse representation based classification (SRC) utilizes the globalmodel for classification. Based on the global-concept of SRC, mean representation classification (MRC) is proposed, which uses the mean vector of each class to constitute the global-model for classification. Motivated by LRC, MRC and SRC, this paper focuses on finding the better global-model for classification. MLRC firstly constitutes regression coefficient matrix and multiple sub-global-models. Afterwards, MLRC computes the weighted value with the coefficient matrix and solve the least square error with multiple sub-global-models for classification. The PolyU Finger-Knuckle-Print (FKP) and Multispectral palm-print (MSP) databases are used to assess the proposed classifier. Experimental results demonstrate that the proposed approach achieves a better recognition rate than the LRC, MRC, SRC and some state-of-the-art methods.