Set-based label propagation of face images

Graph-based Semi-Supervised Learning (SSL) has proven to be an effective tool for label propagation, however, its accuracy is highly dependent on how to form the data weight matrix, in which each element is obtained as the similarity between every pair of data points. Inspired by the success of set-based recognition methods, a novel approach is brought up to incorporate the set-to-set matching as well as single-to-single matching when building up the weight matrix. Canonical Correlation Analysis (CCA), which measures the principal angles between two manifolds, is adopted to compute the set similarity. Moreover, Local Binary Pattern, an effective texture descriptor, is investigated as a data representation to further improve the label propagation performance. The proposed approach is evaluated on two public face image data sets, and shown to significantly outperform the standard SSL methods in terms of accuracy.