Label propagation based on collaborative representation for face recognition

Recently, collaborative representation (CR) has been shown to produce impressive performance on face recognition. However, the performances of CR depend on the number of labeled training samples for each class. When the labeled training samples per class are insufficient, CR would perform inaccurately and correspondingly degrades the final recognition performance. To solve this problem, in this paper, we introduce the CR into semi-supervised learning and propose a novel semi-supervised label propagation approach based on collaborative representation. Based on the subspace assumption that samples of the same class lie in the same subspace, each labeled sample can be well represented by the unlabeled samples of the same class. Our algorithm exploits a large amount of unlabeled samples which contain much more useful information as a dictionary to represent labeled samples, and propagates the label information from labeled data to unlabeled data. Thus, the information of unlabeled data can be effectively explored in our method, which can further improve the performance of collaborative representation with limited labeled training samples. Furthermore, we introduce our label propagation into other semi-supervised learning algorithm to further improve its, recognition performance. Experimental results are presented to demonstrate the efficacy of the proposed method.