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dc.contributor.authorZhou, L.
dc.contributor.authorLiu, Wan-Quan
dc.contributor.authorLu, Z.
dc.contributor.authorNie, T.
dc.identifier.citationZhou, L. and Liu, W. and Lu, Z. and Nie, T. 2014. Face recognition via curvelets and local ternary pattern-based features. IEICE Transactions on Information and Systems. E97-D (4): pp. 1004-1007.

In this Letter, a new face recognition approach based on curvelets and local ternary patterns (LTP) is proposed. First, we observe that the curvelet transform is a new anisotropic multi-resolution transform and can efficiently represent edge discontinuities in face images, and that the LTP operator is one of the best texture descriptors in terms of characterizing face image details. This motivated us to decompose the image using the curvelet transform, and extract the features in different frequency bands. As revealed by curvelet transform properties, the highest frequency band information represents the noisy information, so we directly drop it from feature selection. The lowest frequency band mainly contains coarse image information, and thus we deal with it more precisely to extract features as the face's details using LTP. The remaining frequency bands mainly represent edge information, and we normalize them for achieving explicit structure information. Then, all the extracted features are put together as the elementary feature set. With these features, we can reduce the features' dimension using PCA, and then use the sparse sensing technique for face recognition. Experiments on the Yale database, the extended Yale B database, and the CMU PIE database show the effectiveness of the proposed methods.

dc.publisherMaruzen Co., Ltd.
dc.titleFace recognition via curvelets and local ternary pattern-based features
dc.typeJournal Article
dcterms.source.titleIEICE Transactions on Information and Systems

Copyright © 2014 IEICE

curtin.departmentDepartment of Computing
curtin.accessStatusFulltext not available

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