Abstract
There is a growing interest in subspace learning techniques for face recognition. This paper proposes a novel face recognition method based on MPCA with Gabor tensor representation. Although the Gabor face representation has achieved great success in face recognition, the excessive dimension of the data space often brings the algorithms into the curse of dimensionality dilemma. In this paper, we propose a 3rd-order Gabor tensor representation derived from a complete response set of 40 Gabor filters. Then MPCA (Multi-linear Principal Component Analysis) is applied to each Gabor tensor to extract three discriminative subspaces. The dimension reduction is done in such a way that most useful information is retained. The subspaces are finally integrated for classification. Experimental results on ORL database show promising results of the proposed method.
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Acknowledgments
This project is partly supported by NSF of China (61375001), partly supported by the open fund of Key Laboratory of Measurement and partly supported by Control of Complex Systems of Engineering, Ministry of Education (No. MCCSE2013B01), and partly supported by the open project program of Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University (No. CDLS-2014-04), and partly supported by the China Postdoctoral Science Foundation (2013M540404), and partly supported by the Ph.D. Programs Foundation of Ministry of Education of China (No. 20120092110024).
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Wu, J., Nian, X., Yang, W., Sun, C. (2015). MPCA on Gabor Tensor for Face Recognition. In: Deng, Z., Li, H. (eds) Proceedings of the 2015 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46469-4_45
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DOI: https://doi.org/10.1007/978-3-662-46469-4_45
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