Abstract
There is a growing interest in subspace learning techniques for face recognition such as Gabor face representation. Although the Gabor face representation has received great success in face recognition, the excessive dimension of the data space often brings the algorithms into the curse of dimensionality dilemma. This paper proposes a novel face recognition method based on discriminant analysis with Gabor tensor representation. We derive a 3rd-order Gabor tensor representation from a complete response set of 40 Gabor filters. Then Generalized Principal Component Analysis (GPCA) is applied to each Gabor feature matrix. After working out 40 times, the feature matrices in a lower dimensional subspace are finally integrated for classification. Experimental results on ORL database and AR database show promising results of the proposed method.
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Zhu, L., Huang, R., Ye, X., Yang, W., Changyin, S. (2013). GPCA on Gabor Tensor for Face Recognition. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_44
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DOI: https://doi.org/10.1007/978-3-642-42057-3_44
Publisher Name: Springer, Berlin, Heidelberg
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