Abstract
This paper introduces a novel 2D Gabor-Fisher Classifier for face recognition. The 2D-GFC method applies the 2D Fisher Linear Discriminant Analysis (2D-LDA) to the gaborfaces which is derived from the Gabor wavelets representation of face images. In our method, Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. 2D-LDA is then used to enhance the face recognition performance by maximizing the Fisher’s linear projection criterion. To evaluate the performance of 2D-GFC, experiments were conducted on FERET database with several other methods.
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© 2005 Springer-Verlag Berlin Heidelberg
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Li, M., Yuan, B., Tang, X. (2005). Gabor Feature Based Classification Using 2D Linear Discriminant Analysis for Face Recognition. In: Kanade, T., Jain, A., Ratha, N.K. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2005. Lecture Notes in Computer Science, vol 3546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527923_97
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DOI: https://doi.org/10.1007/11527923_97
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27887-0
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