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Journal of Electronic Imaging • new

Tensor manifold-based extreme learning machine for 2.5-D face recognition
Author(s): Lee Ying Chong; Thian Song Ong; Andrew Beng Jin Teoh
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Paper Abstract

We explore the use of the Gabor regional covariance matrix (GRCM), a flexible matrix-based descriptor that embeds the Gabor features in the covariance matrix, as a 2.5-D facial descriptor and an effective means of feature fusion for 2.5-D face recognition problems. Despite its promise, matching is not a trivial problem for GRCM since it is a special instance of a symmetric positive definite (SPD) matrix that resides in non-Euclidean space as a tensor manifold. This implies that GRCM is incompatible with the existing vector-based classifiers and distance matchers. Therefore, we bridge the gap of the GRCM and extreme learning machine (ELM), a vector-based classifier for the 2.5-D face recognition problem. We put forward a tensor manifold-compliant ELM and its two variants by embedding the SPD matrix randomly into reproducing kernel Hilbert space (RKHS) via tensor kernel functions. To preserve the pair-wise distance of the embedded data, we orthogonalize the random-embedded SPD matrix. Hence, classification can be done using a simple ridge regressor, an integrated component of ELM, on the random orthogonal RKHS. Experimental results show that our proposed method is able to improve the recognition performance and further enhance the computational efficiency.

Paper Details

Date Published: 12 February 2018
PDF: 17 pages
J. Electron. Imag. 27(1) 013016 doi: 10.1117/1.JEI.27.1.013016
Published in: Journal of Electronic Imaging Volume 27, Issue 1
Show Author Affiliations
Lee Ying Chong, Multimedia Univ. (Malaysia)
Thian Song Ong, Multimedia Univ. (Malaysia)
Andrew Beng Jin Teoh, Yonsei Univ. (Republic of Korea)

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