Abstract
Fisher Linear Discriminant Analysis (LDA) has recently been successfully used as a data discriminantion technique. However, LDA-based face recognition algorithms suffer from a small sample size (S3) problem. It results in the singularity of the within-class scatter matrix S w . To overcome this limitation, this paper has developed a novel subspace approach in determining the optimal projection. This algorithm effectively solves the small sample size problem and eliminates the possibility of losing discriminative information.
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Zhao, H., Yuen, P.C., Yang, J. (2004). An Optimal Subspace Analysis for Face Recognition. In: Zhang, D., Jain, A.K. (eds) Biometric Authentication. ICBA 2004. Lecture Notes in Computer Science, vol 3072. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25948-0_14
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DOI: https://doi.org/10.1007/978-3-540-25948-0_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22146-3
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