Abstract
This paper proposes a novel approach of feature extraction called quasi singular values decomposition (QSVD), which can be used to obtain the algebraic features of the original images. An effective classifier, named random weights network (RWN), is applied to improve the learning speed. Integrating QSVD with RWN, fast discrete curvelet transform (FDCT), and 2-dimensional principal component analysis (2DPCA), a new method for face recognition is designed. The experimental results illustrate that the proposed method has an outstanding superiority in the aspects of separability and recognition rate.
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Zhou, Z., Zhao, J., Cao, F. (2013). Face Recognition Based on Random Weights Network and Quasi Singular Value Decomposition. In: Huang, DS., Gupta, P., Wang, L., Gromiha, M. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2013. Communications in Computer and Information Science, vol 375. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39678-6_23
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DOI: https://doi.org/10.1007/978-3-642-39678-6_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39677-9
Online ISBN: 978-3-642-39678-6
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