Abstract
The feature extraction algorithms, which attempt to project the original data contained in a lower dimensional feature space, have drawn much attention. In this paper, based on enhanced fisher discriminant criterion (EFDC), a new feature extraction method called Null Space Diversity Fisher Discriminant Analysis (NSDFDA) is proposed for face recognition. NSDFDA based on a new optimization criterion is presented, which signifies that all the discriminant vectors can be calculated in the null space of the within-class scatter. Moreover, the proposed algorithm is able to extract the orthogonal discriminant vectors in the feature space and simultaneously does not suffer from the small sample size problem, which is desirable for many pattern analysis applications. Experimental results on the Yale database show the effectiveness of the proposed method.
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Acknowledgment
This article is supported by the Nature Science Foundation of China (No. 61471004), the Key Project of Higher Education Natural Science Foundation of Anhui Province (No. KJ2016A203, No. KJ2014A061) and the Master and Doctor Foundation of Anhui University Of Science and Technology (No. 2010yb026).
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Liang, X., Lin, Y., Yang, G., Xu, G. (2017). Null Space Diversity Fisher Discriminant Analysis for Face Recognition. In: Xhafa, F., Patnaik, S., Yu, Z. (eds) Recent Developments in Intelligent Systems and Interactive Applications. IISA 2016. Advances in Intelligent Systems and Computing, vol 541. Springer, Cham. https://doi.org/10.1007/978-3-319-49568-2_45
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DOI: https://doi.org/10.1007/978-3-319-49568-2_45
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