Abstract

This talk explores some principles of unsupervised learning and how they relate to face recognition. Dependency coding and information maximization appear to be central principles in neural coding early in the vidual system. I will argue that these principles may be relevant to how we think about higher visual processes such as face recognition as well. I will first review some examples of dependency learning in biological vision, along with principles of optimal information transfer and information maximization. Next I will describe an algorithm for face recognition by computer that is based on independent component analysis (ICA). This algorithm is based on the principle of optimal information transfer. I will compare it to Eigenfaces. Eigenfaces learns the second-order dependencies among the face image pixels, and maximizes information transfer only in the case where the input distributions are Gaussian. ICA learns the high-order dependencies among the face image pixels as well as the second order ones, and maximizes information transfer for a more general set of input distributions. I show that face representations based on ICA gives better recognition performance than eigenfaces, which supports the theory that dependency learning is a good strategy for high level visual functions such as face recognition. Finally, I review some perceptual studies suggesting that dependency learning is relevant to human face perception as well.