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Multi-view Discriminant Analysis

  • Meina Kan
  • Shiguang Shan
  • Haihong Zhang
  • Shihong Lao
  • Xilin Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7572)

Abstract

The same object can be observed at different viewpoints or even by different sensors, thus generating multiple distinct even heterogeneous samples. Nowadays, more and more applications need to recognize object from distinct views. Some seminal works have been proposed for object recognition across two views and applied to multiple views in some inefficient pairwise manner. In this paper, we propose a Multi-view Discriminant Analysis (MvDA) method, which seeks for a discriminant common space by jointly learning multiple view-specific linear transforms for robust object recognition from multiple views, in a non-pairwise manner. Specifically, our MvDA is formulated to jointly solve the multiple linear transforms by optimizing a generalized Rayleigh quotient, i.e., maximizing the between-class variations and minimizing the within-class variations of the low-dimensional embeddings from both intra-view and inter-view in the common space. By reformulating this problem as a ratio trace problem, an analytical solution can be achieved by using the generalized eigenvalue decomposition. The proposed method is applied to three multi-view face recognition problems: face recognition across poses, photo-sketch face recognition, and Visual (VIS) image vs. Near Infrared (NIR) image face recognition. Evaluations are conducted respectively on Multi-PIE, CUFSF and HFB databases. Intensive experiments show that MvDA can achieve a more discriminant common space, with up to 13% improvement compared with the best known results.

Keywords

Multi-view Discriminant Analysis Multi-view Face Recognition Common space for Multi-view 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Meina Kan
    • 1
  • Shiguang Shan
    • 1
  • Haihong Zhang
    • 2
  • Shihong Lao
    • 2
  • Xilin Chen
    • 1
  1. 1.Institute of Computing Technology, CASKey Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS)BeijingChina
  2. 2.Omron Social Solutions Co., LTD.KyotoJapan

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