Robust pose invariant face recognition using coupled latent space discriminant analysis

Abhishek Sharma, Murad Al Haj, Jonghyun Choi, Larry S. Davis, David W. Jacobs

Research output: Contribution to journalArticlepeer-review

73 Citations (Scopus)


We propose a novel pose-invariant face recognition approach which we call Discriminant Multiple Coupled Latent Subspace framework. It finds the sets of projection directions for different poses such that the projected images of the same subject in different poses are maximally correlated in the latent space. Discriminant analysis with artificially simulated pose errors in the latent space makes it robust to small pose errors caused due to a subject's incorrect pose estimation. We do a comparative analysis of three popular latent space learning approaches: Partial Least Squares (PLSs), Bilinear Model (BLM) and Canonical Correlational Analysis (CCA) in the proposed coupled latent subspace framework. We experimentally demonstrate that using more than two poses simultaneously with CCA results in better performance. We report state-of-the-art results for pose-invariant face recognition on CMU PIE and FERET and comparable results on MultiPIE when using only four fiducial points for alignment and intensity features.

Original languageEnglish
Pages (from-to)1095-1110
Number of pages16
JournalComputer Vision and Image Understanding
Issue number11
Publication statusPublished - 2012 Nov

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition


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