Optimal FLD algorithm for facial feature extraction

Jian Yang; Jingyu Yang

doi:10.1117/12.444212

5 October 2001 Optimal FLD algorithm for facial feature extraction

Jian Yang, Jingyu Yang

Proceedings Volume 4572, Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision; (2001) https://doi.org/10.1117/12.444212
Event: Intelligent Systems and Advanced Manufacturing, 2001, Boston, MA, United States

Abstract

In this paper, we try to extend Fisher linear discriminant analysis (FLD) to the singular cases. Firstly, PCA is used to reduce the dimension of feature space to N-1 (N denotes the number of training samples). Then, the transformed space is divided into two subspaces: the null space of within- class scatter matrix and its orthogonal complement, from which two cases of optimal discriminant vectors are selected respectively. Finally, we test our method on ORL face database, and achieve a recognition rate of 97% with a minimum distance classifier or a nearest neighbor classifier. The experimental results indicate that our approach is better than classical Eigenfaces and Fisherfaces with respect to recognition performance.

Citation Download Citation

Jian Yang and Jingyu Yang "Optimal FLD algorithm for facial feature extraction", Proc. SPIE 4572, Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision, (5 October 2001); https://doi.org/10.1117/12.444212

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