Kernel-based Regularized Neighbourhood Preserving Embedding in face recognition


Pang, Ying Han and Teoh, Andrew Beng Jin (2012) Kernel-based Regularized Neighbourhood Preserving Embedding in face recognition. In: 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE Xplore, pp. 883-888. ISBN 978-1-4577-2118-2

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Face images always have significant intra-class variations due to different poses, illuminations and facial expressions. These variations trigger substantial deviation from the linearity assumption of data structure, which is essential in formulating linear dimension reduction technique. In this paper, we present a kernel based regularized graph embedding dimension reduction technique, known as kernel-based Regularized Neighbourhood Preserving Embedding (KRNPE) to address this problem. KRNPE first exploits kernel function to unfold the nonlinear intrinsic facial data structure. Neighbourhood Preserving Embedding, a graph embedding based linear dimension reduction technique, is then regulated based on Adaptive Locality Preserving Regulation Model, established in [7] to enhance the locality preserving capability of the projection features, leading to better discriminating capability and generalization performance. Experimental results on PIE and FERET face databases validate the effectiveness of KRNPE.

Item Type: Book Section
Uncontrolled Keywords: kernelization; graph embedding; Adaptive Locality Preserving Regulation Model; class discrimination; face recognition
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 06 Jan 2014 04:45
Last Modified: 05 Jan 2017 04:42


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