Robust pose invariant face recognition using coupled latent space discriminant analysis

We propose a novel pose-invariant face recognition approach which we call Dis-criminant Multiple Coupled Latent Subspace framework. It finds sets of pro-jection directions for different poses such that the projected images of the same subject 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 com-parative analysis of three popular learning approaches: Partial Least Squares (PLS), Bilinear Model (BLM) and Canonical Correlational Analysis (CCA) in the proposed coupled latent subspace framework. We also show that using more than two poses simultaneously with CCA results in better performance. We re-port state-of-the-art results for pose-invariant face recognition on CMU PIE and FERET and comparable results on MultiPIE when using only 4 fiducial points and intensity features