  Developing a reliable and practical face recognition system is a
long-standing goal in computer vision research. Existing literature suggests
that pixel-wise face alignment is the key to achieve high-accuracy face
recognition. By assuming a human face as piece-wise planar surfaces, where each
surface corresponds to a facial part, we develop in this paper a Constrained
Part-based Alignment (CPA) algorithm for face recognition across pose and/or
expression. Our proposed algorithm is based on a trainable CPA model, which
learns appearance evidence of individual parts and a tree-structured shape
configuration among different parts. Given a probe face, CPA simultaneously
aligns all its parts by fitting them to the appearance evidence with
consideration of the constraint from the tree-structured shape configuration.
This objective is formulated as a norm minimization problem regularized by
graph likelihoods. CPA can be easily integrated with many existing classifiers
to perform part-based face recognition. Extensive experiments on benchmark face
datasets show that CPA outperforms or is on par with existing methods for
robust face recognition across pose, expression, and/or illumination changes.

Computer Vision and Pattern Recognition;Machine Learning