Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Kernel independent component analysis
The Journal of Machine Learning Research
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Nonlinear kernel-based statistical pattern analysis
IEEE Transactions on Neural Networks
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighbourhood preserving discriminant embedding in face recognition
Journal of Visual Communication and Image Representation
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In this paper, we introduce a new feature representation method for face recognition. The proposed method, referred as Kernel ICA, combines the strengths of the Kernel and Independent Component Analysis approaches. For performing Kernel ICA, we employ an algorithm developed by F. R. Bach and M. I. Jordan. This algorithm has proven successful for separating randomly mixed auditory signals, but it has never been applied on bidimensional signals such as images. We compare the performance of Kernel ICA with classical algorithms such as PCA and ICA within the context of appearance-based face recognition problem using the FERET database. Experimental results show that both Kernel ICA and ICA representations are superior to representations based on PCA for recognizing faces across days and changes in expressions.