Fuzzy weighted sparse reconstruction error-steered semi-supervised learning for face recognition

Li Liu, Siqi Chen, Xiuxiu Chen, Tianshi Wang, Long Zhang

Abstract: Since the number of labeled data is limited in the semi-supervised learning settings, we propose a fuzzy weighted sparse reconstruction error-steered semi-supervised learning method for face recognition. The fuzzy membership functions are introduced to the reconstruction error calculation for the unlabeled data. A weight function is utilized to capture the locality property of data when learning the sparse coefficients. The fuzzy weighted sparse reconstruction error-steered semi-supervised learning not only inherits the advantages of sparse representation classification techniques and neighborhood methods, but also steers the reconstruction errors of unlabeled data. Experimental studies on well-known face image datasets demonstrate that the proposed method outperforms the comparative approaches.