Abstract
The null space N(S t ) of total scatter matrix S t contains no useful information for pattern classification. So, discarding the null space N(S t ) results in dimensionality reduction without loss discriminant power. Combining this subspace technique with proposed rank lifting scheme, a new regularized Fisher discriminant (SL-RFD) method is developed to deal with the small sample size (S3) problem in face recognition. Two public available databases, namely FERET and CMU PIE databases, are exploited to evaluate the proposed algorithm. Comparing with existing LDA-based methods in solving the S3 problem, the proposed SL-RFD method gives the best performance.
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Chen, WS., Yuen, P.C., Huang, J., Lai, J., Tang, J. (2005). A Novel Regularized Fisher Discriminant Method for Face Recognition Based on Subspace and Rank Lifting Scheme. In: Tao, J., Tan, T., Picard, R.W. (eds) Affective Computing and Intelligent Interaction. ACII 2005. Lecture Notes in Computer Science, vol 3784. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573548_20
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DOI: https://doi.org/10.1007/11573548_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29621-8
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