Collaborative probabilistic labels for face recognition from single sample per person

• Published in: Pattern recognition Vol. 62; pp. 125 - 134
• Main Authors: Ji, Hong-Kun, Sun, Quan-Sen, Ji, Ze-Xuan, Yuan, Yun-Hao, Zhang, Guo-Qing
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2017
• Subjects: Collaborative representation; Face recognition; Label propagation; Probabilistic graph; Single training sample per person; Single training sample per person; Collaborative representation; Probabilistic graph; Face recognition; Label propagation
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2016.08.007

Single sample per person (SSPP) recognition is one of the most challenging problems in face recognition (FR) due to the lack of information to predict the variations in the query sample. To address this problem, we propose in this paper a novel face recognition algorithm based on a robust collaborative representation (CR) and probabilistic graph model, which is called Collaborative Probabilistic Labels (CPL). First, by utilizing label propagation, we construct probabilistic labels for the samples in the generic training set corresponding to those in the gallery set, thus the discriminative information of the unlabeled data can be effectively explored in our method. Then, the adaptive variation type for a given test sample is automatically estimated. Finally, we propose a novel reconstruction-based classifier for the test sample with its corresponding adaptive dictionary and probabilistic labels. The proposed probabilistic graph based model is adaptively robust to various variations in face images, including illumination, expression, occlusion, pose, etc., and is able to reduce required training images to one sample per class. Experimental results on five widely used face databases are presented to demonstrate the efficacy of the proposed approach. •Constructed probabilistic graph propagates discrimination from generic to gallery.•The adaptive variation type for a given sample can be automatically estimated.•CPL incorporates a novel probabilistic label reconstruction based.