Adaptation and decorrelation in the cortex
The computing neuron
EMPATH: face, emotion, and gender recognition using holons
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Learning invariance from transformation sequences
Neural Computation
Elements of information theory
Elements of information theory
What does the retina know about natural scenes?
Neural Computation
What is the goal of sensory coding?
Neural Computation
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information-theoretic approach to blind separation of sources in non-linear mixture
Signal Processing - Special issue on neural networks
Support vector machines applied to face recognition
Proceedings of the 1998 conference on Advances in neural information processing systems II
Face Recognition Based on ICA Combined with FLD
ECCV '02 Proceedings of the International ECCV 2002 Workshop Copenhagen on Biometric Authentication
A deformable model for the recognition of human faces under arbitrary illumination
A deformable model for the recognition of human faces under arbitrary illumination
Recognizing faces with PCA and ICA
Computer Vision and Image Understanding - Special issue on Face recognition
Learning Overcomplete Representations
Neural Computation
Journal of Cognitive Neuroscience
Neural Computation
Modeling face appearance with nonlinear independent component analysis
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
A local spectral distribution approach to face recognition
Computer Vision and Image Understanding
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This perspective paper explores 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 visual system. These principles may be relevant to how we think about higher visual processes such as face recognition as well. The paper first reviews examples of dependency learning in biological vision, along with principles of optimal information transfer and information maximization. Next, we examine algorithms for face recognition by computer from a perspective of information maximization. The eigenface approach can be considered as an unsupervised system that learns the first- and second-order dependencies among face image pixels. Eigenfaces maximize information transfer only in the case where the input distributions are Gaussian. Independent component analysis (ICA) learns high-order dependencies in addition to first- and second-order relations, and maximizes information transfer for a more general set of input distributions. Face representations based on ICA gave better recognition performance than eigenfaces, supporting the theory that information maximization is a good strategy for high level visual functions such as face recognition. Finally, we review perceptual studies suggesting that dependency learning is relevant to human face perception as well, and present an information maximization account of perceptual effects such as the atypicality bias, and face adaptation aftereffects.