Face Recognition via Curvelets and Local Ternary Pattern-Based Features

Lijian ZHOU; Wanquan LIU; Zhe-Ming LU; Tingyuan NIE

doi:10.1587/transinf.E97.D.1004

Face Recognition via Curvelets and Local Ternary Pattern-Based Features

Lijian ZHOU, Wanquan LIU, Zhe-Ming LU, Tingyuan NIE

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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.

Publication: IEICE TRANSACTIONS on Information Vol.E97-D No.4 pp.1004-1007

Publication Date: 2014/04/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1587/transinf.E97.D.1004

Type of Manuscript: LETTER

Category: Image Recognition, Computer Vision

Authors

Lijian ZHOU
  Qingdao Technological University
Wanquan LIU
  Curtin University
Zhe-Ming LU
  Zhejiang University
Tingyuan NIE
  Qingdao Technological University

Keyword

face recognition, curvelet transform, local ternary pattern, sparse representation based classification

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Lijian ZHOU, Wanquan LIU, Zhe-Ming LU, Tingyuan NIE, "Face Recognition via Curvelets and Local Ternary Pattern-Based Features" in IEICE TRANSACTIONS on Information, vol. E97-D, no. 4, pp. 1004-1007, April 2014, doi: 10.1587/transinf.E97.D.1004.
Abstract: 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.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.E97.D.1004/_p

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@ARTICLE{e97-d_4_1004,
author={Lijian ZHOU, Wanquan LIU, Zhe-Ming LU, Tingyuan NIE, },
journal={IEICE TRANSACTIONS on Information},
title={Face Recognition via Curvelets and Local Ternary Pattern-Based Features},
year={2014},
volume={E97-D},
number={4},
pages={1004-1007},
abstract={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.},
keywords={},
doi={10.1587/transinf.E97.D.1004},
ISSN={1745-1361},
month={April},}

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TY - JOUR
TI - Face Recognition via Curvelets and Local Ternary Pattern-Based Features
T2 - IEICE TRANSACTIONS on Information
SP - 1004
EP - 1007
AU - Lijian ZHOU
AU - Wanquan LIU
AU - Zhe-Ming LU
AU - Tingyuan NIE
PY - 2014
DO - 10.1587/transinf.E97.D.1004
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2014
AB - 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.
ER -