Face recognition via semi-supervised discriminant local analysis


Goh, Fan Ling and Pang, Ying Han and Khor, Ean Yee and Ooi, Shih Yin (2016) Face recognition via semi-supervised discriminant local analysis. In: 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). IEEE, pp. 292-297. ISBN 978-1-4799-8996-6

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Semi-supervised learning approach is a fusion approach of supervised and unsupervised learning. Semi-supervised approach performs data learning from a limited number of available labelled training images along with a large pool of unlabelled data. Semi-supervised discriminant analysis (SDA) is one of the popular semi-supervised techniques. However, there is room for improvement. SDA resides in the illumination and local change of the face features. Hence, it is hardly to guarantee its performance if there are illumination and local changes on the images. This paper presents an improved version of SDA, termed as Semi-Supervised Discriminant Local Analysis (SDLA). In this proposed technique, a local descriptor is amalgamated with SDA. Hence, SDLA could possess the capabilities of both the local descriptor and SDA, in such a way that SDLA utilizes limited number of labelled training data and huge pool of unlabelled data to optimally capture local discriminant features of face data. The empirical results demonstrate that SDLA shows promising performance in both normal and makeup face authentication.

Item Type: Book Section
Uncontrolled Keywords: semi-supervised discriminant analysis, Normal and makeup face recognition, semi-supervised learning, local descriptor, histogram of oriented gradient
Subjects: H Social Sciences > H1-99 Social Sciences (General)
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 08 Dec 2017 14:39
Last Modified: 08 Dec 2017 14:39
URII: http://shdl.mmu.edu.my/id/eprint/6585


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