Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Image Analysis by Unsupervised Learning
Face Image Analysis by Unsupervised Learning
The Fixed-Point Algorithm and Maximum Likelihood Estimation forIndependent Component Analysis
Neural Processing Letters
Recognizing faces with PCA and ICA
Computer Vision and Image Understanding - Special issue on Face recognition
Journal of Cognitive Neuroscience
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
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In order for a subspace projection based method to be robust to local distortion and partial occlusion, the basis images generated by the method should exhibit a part-based local representation. We propose an effective part-based local representation method using ICA architecture I basis images that is robust to local distortion and partial occlusion. The proposed representation only employs locally salient information from important facial parts in order to maximize the benefit of applying the idea of “recognition by parts.” We have contrasted our representation with other part-based representations such as LNMF (Localized Non-negative Matrix Factorization) and LFA (Local Feature Analysis). Experimental results show that our representation performs better than PCA, ICA architecture I, ICA architecture II, LFA, and LNMF methods, especially in the cases of partial occlusions and local distortions.