Multimodal preserving embedding for face recognition

Traditional dimension reduction approaches always consider the samples in a class are uni-modal. In real world, samples in a class are usually multi-modal, for instance, the manifold of the facial appearance of a person under different illumination, expression, and poses is multi-modal. Recently, dimension reduction approaches based on manifold learning are presented, the main purpose is to preserve the manifold structure on low dimensional space. In this paper, by analyzing the manifold learning methods and the traditional dimension reduction methods, we show that most of these methods can be summarized into a general framework. Based on this framework, we propose a novel dimension reduction approach, called multi-modal preserving embedding (MPE) by utilizing path-based similarity measure. We also describe two useful extensions of our method: KernelMPE and TensorMPE. Comprehensive comparisons and extensive experiments on face recognition are included to demonstrate the effectiveness of our method.