Abstract
In this paper, we present a new statistical projection-based face recognition method, called Bilinear Discriminant Analysis (BDA). The proposed technique effectively combines two complementary versions of Two-Dimensional-Oriented Linear Discriminant Analysis (2DoLDA), namely Column-Oriented Linear Discriminant Analysis (CoLDA) and Row-Oriented Linear Discriminant Analysis (RoLDA). BDA relies on the maximization of a generalized bilinear projection-based Fisher criterion. A series of experiments was performed on various international face image databases in order to evaluate and compare the effectiveness of BDA to RoLDA and CoLDA. The experimental results indicate that BDA outperforms RoLDA, CoLDA and 2DPCA for face recognition, while leading to a significant dimensionality reduction.
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Visani, M., Garcia, C., Jolion, JM. (2005). Bilinear Discriminant Analysis for Face Recognition. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_28
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DOI: https://doi.org/10.1007/11552499_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28833-6
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