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IEEE Transactions on Pattern Analysis and Machine Intelligence
Independent component analysis, a new concept?
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IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast fixed-point algorithm for independent component analysis
Neural Computation
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Independent component analysis: algorithms and applications
Neural Networks
Principal Manifolds and Probabilistic Subspaces for Visual Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel independent component analysis
The Journal of Machine Learning Research
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IEEE Transactions on Pattern Analysis and Machine Intelligence
A Unified Framework for Subspace Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Is ICA Significantly Better than PCA for Face Recognition?
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Independent component analysis in a facial local residue space
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Feature extraction based on Laplacian bidirectional maximum margin criterion
Pattern Recognition
Feature extraction using two-dimensional neighborhood margin and variation embedding
Computer Vision and Image Understanding
Face recognition using Weber local descriptors
Neurocomputing
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This paper presents a novel subspace method called sequential row-column independent component analysis (RC-ICA) for face recognition. Unlike the traditional ICA, in which the face image is transformed into a vector before calculating the independent components (ICs), RC-ICA consists of two sequential stages-an image row-ICA followed by a column-ICA. There is no image-to-vector transformation in both the stages and the ICs are computed directly in the subspace spanned by the row or column vectors. RC-ICA can reduce the face recognition error caused by the dilemma in traditional ICA, i.e. the number of available training samples is greatly less than that of the dimension of training vector. Another advantage of RC-ICA over traditional ICA is that the dimensionality of the recognition subspace is much smaller, which means that the face image can have a more condensed representation. Extensive experiments are performed on the well-known Yale-B, AR and FERET databases to validate the proposed method and the experimental results show that the RC-ICA achieves higher recognition accuracy than ICA and other existing subspace methods while using a subspace of smaller dimensionality.