{"id":6524,"date":"2026-06-20T17:15:29","date_gmt":"2026-06-20T09:15:29","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=6524"},"modified":"2026-08-21T00:39:27","modified_gmt":"2026-08-20T16:39:27","slug":"three-dimension-face-recognition-based-on-gabor-transformation-and-support-vector-machine","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=three-dimension-face-recognition-based-on-gabor-transformation-and-support-vector-machine","title":{"rendered":"Three Dimension Face Recognition Based on Gabor Transformation and Support Vector Machine"},"content":{"rendered":"\n
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<\/i> 2019<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 22, Issue 1<\/a><\/p>\n<\/div>\n\n\n\n

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Delong Zhang1<\/sup>, Min Zhang2<\/sup> and Yongbo Liu3<\/sup><\/i><\/a><\/p>\n\n\n\n

1School of Electric Automation, Weifang University of Science and Technology, Weifang 262700, P.R. China <\/p>\n\n\n\n

2Department of Mechanical and Electronic Engineering, Shandong Management University, Ji\u2019nan 250357, P.R. China <\/p>\n\n\n\n

3Department of City Management, Hunan City University, Yiyang, Hunan 413000, P.R. China<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0December 30, 2016
Accepted:\u00a0October 28, 2018
Publication Date:\u00a0June 20, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

The proposed 3D face recognition.<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:  BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.201903_22(1).0016<\/a>  <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Face recognition based on 2D face images is still challenged by the large change of illumination, pose and expression after more than 10 years\u2019 research and its recognition rate is still far away from satisfaction under the change of the above three factors. Gabor wavelet and support vector machine
(SVM) are studied. Gabor wavelet has good biological neurons function, and good adaptive change to illumination. Furthermore, SVM can provide good classification effect. Gabor wavelet and SVM are combined with PCA to improve recognition rate of humanface. Besides, we put forward a kind of Gabor wavelet feature extraction method based on three-dimensional human face contour line. Five different types of human face recognition are used to verify the effectiveness of this algorithm. The experiment results show that the proposed scheme has higher recognition rate than several traditional schemes.<\/p>\n\n\n\n

Keywords:\u00a0Three Dimension Face Recognition, Gabor Wavelet, Support Vector Machine, Feature Extraction<\/em><\/p>\n\n\n\n

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