Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
What does the retina know about natural scenes?
Neural Computation
A fast fixed-point algorithm for independent component analysis
Neural Computation
Independent component analysis: algorithms and applications
Neural Networks
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using IPCA-ICA Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Neural Computation
Representing image matrices: eigenimages versus eigenvectors
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
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In this paper, a new feature extraction algorithm considering both two-directional two-dimensional principal component analysis ((2D)2PCA) and independent component analysis(ICA), called (2D)2PCA-ICA, is proposed for face representation. This algorithm analyzes the principal components of image vectors on 2D matrices by simultaneously considering the row and column directions as opposed to the standard PCA based on 1D vectors, and transforming those principal components to the independent components that maximize the non-Gaussianity of the sources. These two major techniques such as (2D)2PCA and ICA are used sequentially in order to obtain the most efficient features that properly describe a whole set of human faces in face databases. The proposed algorithm is applied to the face recognition problem. Simulation results on ORL and Yale B face databases shows that the proposed algorithm achieves high average success rate in face recognition compared with other models.