Abstract
In this paper, a novel discriminant analysis named two-dimensional Heteroscedastic Discriminant Analysis (2DHDA) is presented for face recognition. In 2DHDA, small sample size problem (S3 problem) of Heteroscedastic Discriminant Analysis (HAD) is overcome. Firstly, the criterion of 2DHDA is defined according to that of 2DLDA. Secondly, criterion of 2DHDA, log and rearranging terms are taken, and then the optimal projection matrix is solved by gradient descent algorithm. Thirdly, face images are projected onto the optimal projection matrix, thus the 2DHDA features are extracted. Finally, Nearest Neighbor classifier is selected to perform face recognition. Experimental results show that higher recognition rate is obtained by way of 2DHDA compared with 2DLDA.
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Zeng, JY., Gan, JY., He, SB. (2010). Applications of Two-Dimensional Heteroscedastic Discriminant Analysis in Face Recognition. In: Huang, DS., McGinnity, M., Heutte, L., Zhang, XP. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Communications in Computer and Information Science, vol 93. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14831-6_39
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DOI: https://doi.org/10.1007/978-3-642-14831-6_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14830-9
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