Local median based linear regression classification for biometric recognition

ABSTR A C T As an efficacy classification technique, linear regression based classification (LRC) has been popularly used for image recognition. However, since LRC neglects the local structure as well as dissimilarities between different classes, its performance may degrade in many real-world ap-plications. To alleviate this problem, a robust classification method, namely local median based linear regression classification (LMLRC) is put forward in this paper. In the proposed model, based on a concept that the samples of one class having more similar features should be nearer and to handle errors brought by noise and variations between images, the test sample is linearly coded by its local median vectors of all classes; then the test sample is classified to the class which owns the biggest element in the coefficient vector. Extensive experiments on AR, CMU PIE, FRGC and PolyU Palmprint datasets show that the presented approach has superior performance to some other state-of-the-art classification algorithms.