Transform-invariant dictionary learning for face recognition

Dictionary learning has important applications in face recognition. However, large transformation variations of face images pose a grand challenge to conventional dictionary learning methods. A large portion of misleading dictionary atoms are usually learned to represent transformation factors, which will cause ambiguity in face recognition. To address this problem, this paper proposes a general framework for transform-invariant basis matrix learning. Specifically, we present a transform-invariant dictionary learning method which explicitly incorporates an appearance consistent error term to the original objective function in dictionary learning. The unified objective function is effectively optimized in an alternating iterative way. An ensemble of aligned images and a discriminative transform-invariant dictionary for sparse coding can be obtained by solving the formulated objective function. Experimental results on two public face databases demonstrate our algorithm's superiority compared with two state-of-the-art dictionary learning methods and the recently proposed transform-invariant PCA method.