  The pose problem is one of the bottlenecks in automatic face recognition. We
argue that one of the diffculties in this problem is the severe misalignment in
face images or feature vectors with different poses. In this paper, we propose
that this problem can be statistically solved or at least mitigated by
maximizing the intra-subject across-pose correlations via canonical correlation
analysis (CCA). In our method, based on the data set with coupled face images
of the same identities and across two different poses, CCA learns
simultaneously two linear transforms, each for one pose. In the transformed
subspace, the intra-subject correlations between the different poses are
maximized, which implies pose-invariance or pose-robustness is achieved. The
experimental results show that our approach could considerably improve the
recognition performance. And if further enhanced with holistic+local feature
representation, the performance could be comparable to the state-of-the-art.
