Comparing Kernel-based Learning Methods for Face Recognition
نویسنده
چکیده
Principal Component Analysis (PCA) and Fisher Discriminant Analysis (FDA) have been successfully applied to face recognition, and both are based on the second order statistics of the image set. Kernel-based subspace methods try to capture the higher order statistics of the image set and thus may provide better results for recognition purposes. In this paper, we try to compare different algorithms for face recognition and find out if kernel-based methods are better than linear version methods for face recognition, and we also explore the experimental results.
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تاریخ انتشار 2003