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Independent component analysis for face authentication

Independent component analysis (ICA) is presented as an alternative feature extraction algorithm to principal component analysis (PCA) widely used in automatic face recognition/authentication tasks. We show that the promising ICA algorithm extracts from faces features that are relevant and efficient for authentication. This leads to improved success rates and a reduced client model size over a PCA based feature extraction.