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Wavelet Based SDA for Face Recognition

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Neural Information Processing (ICONIP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8836))

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Semi-supervised discriminant analysis (SDA) is a popular semi-supervise learning technique for limited labelled training sample problem in face recognition. However, SDA resides in the illumination variations and noise of the face features. Hence, SDA exposes the illumination variations and noise when constructing the optimal projection. It could affect the projection, leading to poor performance. In this paper, an enhanced SDA, namely Wavelet SDA, is proposed. This proposed technique is to resolve the problem of intra-class variations due to illumination variations and noise on image data. The robustness of the proposed technique is evaluated using three well-known face databases, i.e. ORL, FERET and FRGC. Empirical results validated the good effects of wavelet transform on SDA, leading to better recognition performance.

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Ling, G.F., Han, P.Y., Ping, L.Y., Yin, O.S., Kiong, L.C. (2014). Wavelet Based SDA for Face Recognition. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8836. Springer, Cham.

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  • Print ISBN: 978-3-319-12642-5

  • Online ISBN: 978-3-319-12643-2

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