A Least-Squares Based Two-Phase Face Recognition Method

Abstract

In this paper, an iterative method for solving linear systems and min is used to calculate the best representations of the test sample as a linear combination of all the training samples. Then a least-squares Based two-phase face recognition algorithm is proposed. This algorithm is as follows: its first phase uses a least-squares method to calculate the contribution between a test sample and each sample in the training sets, and then exploits the contribution of each training sample to determine K nearest neighbors for the test sample. Its second phase represents the test sample as a linear combination of the determined K nearest neighbors and uses the representation result to perform classification. The experimental results show that our method outperforms the two-phase test sample sparse representation methods for use with face recognition (TPTSR).