Wavelet based SDA for face recognition

Goh, Fang Ling and Pang, Ying Han and Liew, Yee Ping and Ooi, Shih Yin and Loo, Chu Kiong (2014) Wavelet based SDA for face recognition. In: Neural Information Processing. Lecture Notes in Computer Science . Springer International Publishing, pp. 628-635. ISBN 978-3-319-12643-2

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Abstract

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.

Item Type: Book Section
Additional Information: Book Subtitle: 21st International Conference, ICONIP 2014, Kuching, Malaysia, November 3-6, 2014. Proceedings, Part III
Uncontrolled Keywords: face recognition, semi-supervised, wavelet transform
Subjects: Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 23 Jan 2015 06:53
Last Modified: 05 Jan 2017 04:38
URI: http://shdl.mmu.edu.my/id/eprint/5946

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