Class-label locally linear embedding in face recognition


Pang, Ying Han and Ooi, Shih Yin and Abas, Fazly Salleh and Teoh, Andrew Ben Jin (2009) Class-label locally linear embedding in face recognition. Bahria University Journal of Information & Communication Technology (BUJICT), 2 (1). pp. 18-25. ISSN 1999‐4974

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Locally Linear Embedding (LLE) is an unsupervised non-linear manifold learning method, which has spurred increased interest in face recognition research recently. However, it is commonly known that a supervised method that considering the class-specific information always outperforms the unsupervised one, especially in biometric recognition task. In this paper, we propose a supervised LLE technique, known as class-label Locally Linear Embedding (cLLE). cLLE aims to discover the nonlinearity of high-dimensional data by minimizing the global reconstruction error of the set of all local neighbours in the data set. cLLE method is using user class-specific information in neighbourhoods selection and thus preserves the local neighbourhoods. Since the locality preservation is correlated to the class discrimination, the proposed cLLE is expected superior to LLE in face recognition. Experimental results on three face databases demonstrate the success of the proposed technique.

Item Type: Article
Uncontrolled Keywords: Face recognition, Locally Linear Embedding, FisherSpace, class-specific information
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Engineering and Technology (FET)
Faculty of Information Science and Technology (FIST)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 22 Jan 2014 04:50
Last Modified: 19 Aug 2021 05:46


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