Margin Based Semi-Supervised Elastic Embedding for Face Image Analysis

This paper introduces a graph-based semi-supervised elastic embedding method as well as its kernelized version for face image embedding and classification. The proposed frameworks combines Flexible Manifold Embedding and non-linear graph based embedding for semi-supervised learning. In both proposed methods, the nonlinear manifold and the mapping (linear transform for the linear method and the kernel multipliers for the kernelized method) are simultaneously estimated, which overcomes the shortcomings of a cascaded estimation. Unlike many state-of-the art non-linear embedding approaches which suffer from the out-of-sample problem, our proposed methods have a direct out-of-sample extension to novel samples. We conduct experiments for tackling the face recognition and image-based face orientation problems on four public databases. These experiments show improvement over the state-of-the-art algorithms that are based on label propagation or graph-based semi-supervised embedding.