Margin Based Semi-Supervised Elastic Embedding for Face Image Analysis

Fadi Dornaika, Youssof El Traboulsi; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1313-1320

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 non-linear 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.