IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape-Based Human Activity Recognition Using Independent Component Analysis and Hidden Markov Model
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Global and local preserving feature extraction for image categorization
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Lighting and pose robust face sketch synthesis
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
The small sample size problem of ICA: A comparative study and analysis
Pattern Recognition
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The standard PCA was always used as baseline algorithm to evaluate ICA-based face recognition systems in the previous research. In this paper, we examine the two architectures of ICA for image representation and find that ICA Architecture I involves a PCA process by vertically centering (PCA I), while ICA Architecture II involves a whitened PCA process by horizontally centering (PCA II). So, it is reasonable to use these two PCA versions as baseline algorithms to revaluate the ICA-based face recognition systems. The experiments were performed on the FERET face database. The experimental results show there is no significant performance differences between ICA Architecture I (II) and PCA I (II), although ICA Architecture II significantly outperforms the standard PCA. It can be concluded that the performance of ICA strongly depends on its involved PCA process. The pure ICA projection has little effect on the performance of face recognition.