An Algorithm for the Learning of Weights in Discrimination Functions Using a Priori Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection for Pose Invariant Face Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Face recognition via direct search optimized Gabor filters
ICOSSE'06 Proceedings of the 5th WSEAS international conference on System science and simulation in engineering
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In this paper, a two–level supervised feature selection algorithm for local feature–based face recognition is presented. In the first part, a genetic algorithm is used to determine the useful locations of the face region for recognition. 2D Gabor wavelet–based feature extractors are used for local image descriptors at these locations. In the second part, the most useful frequencies and orientations of Gabor kernels are determined using a floating feature selection algorithm. Our major aim in this study is to examine the relevance of the two common assumptions in the local feature based face recognition literature: first, that the contribution of a specific feature to the recognition performance is independent of others, and secondly, that feature extractors should be placed over the visually salient points. In this paper, we show that one can obtain better recognition accuracy by relaxing these two assumptions.