We propose a novel pose-invariant face recognition approach which we call Discriminant Multiple Coupled Latent Subspace framework. It finds sets of projection 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 comparative 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 report 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.
In REF , Sharma at al. propose the Discriminant Multiple Coupled Latent Subspace framework.
Robust pose invariant face recognition using coupled latent space discriminant analysis
Comput. Vis. Image Underst.
Comput. Vis. Image Underst.
Mathematics Computer Science