Score level fusion in representation-based classification method for face recognition. (June 2020) - Li, Zi-Qi; Sun, Jun; Wu, Xiao-Jun; Yin, He-Feng
Recent years have witnessed the success of representation-based classification method (RBCM) in the domain of face recognition. Collaborative representation-based classification (CRC) and linear regression-based classification (LRC) are two representative approaches. CRC is a global representation method which uses all training samples to represent test samples and utilizes representation residuals to perform classification, whereas LRC is a local representation method which exploits training samples from each class to represent test samples. Related researches indicate that the combination of LRC and CRC can fully exploit the representation residuals produced by them, thus improving the performance of RBCM. However, the representation coefficients obtained by CRC usually contain negative values which may result in overfitting problem. Therefore, to solve this problem to some extent, the combination of LRC and non-negative least square-based classification (NNLSC) is proposed in this paper. Experimental results on benchmark face datasets show that the proposed method is superior to the combination of LRC and CRC and other state-of-the-art RBCMs. The source code of our proposed method is available athttps://github.com/li-zi-qi/score-level-fusion-of-NNLS-and-LRC .