Nonlinear dimension reduction using ISOMap based on class information
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Outlier-resisting graph embedding
Neurocomputing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Face recognition using Zernike and complex Zernike moment features
Pattern Recognition and Image Analysis
Facial image analysis using subspace segregation based on class information
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Object categorization with sketch representation and generalized samples
Pattern Recognition
| Hi-index | 0.00 |
The literature on independent component analysis (ICA)-based face recognition generally evaluates its performance using standard principal component analysis (PCA) within two architectures, ICA Architecture I and ICA Architecture II. In this correspondence, we analyze these two ICA architectures and find that ICA Architecture I involves a vertically centered PCA process (PCA I), while ICA Architecture II involves a whitened horizontally centered PCA process (PCA II). Thus, it makes sense to use these two PCA versions as baselines to reevaluate the performance of ICA-based face-recognition systems. Experiments on the FERET, AR, and AT&T face-image databases showed no significant differences between ICA Architecture I (II) and PCA I (II), although ICA Architecture I (or II) may, in some cases, significantly outperform standard PCA. It can be concluded that the performance of ICA strongly depends on the PCA process that it involves. Pure ICA projection has only a trivial effect on performance in face recognition.