Facial Expression Recognition Based on Local Texture and Shape Features

HU Min; TENG Wendi; WANG Xiaohua; XU Liangfeng; YANG Juan

doi:10.11999/JEIT170799

Volume 40 Issue 6

May 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(6): 1338-1344

HU Min, TENG Wendi, WANG Xiaohua, XU Liangfeng, YANG Juan. Facial Expression Recognition Based on Local Texture and Shape Features[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1338-1344. doi: 10.11999/JEIT170799

Citation:

HU Min, TENG Wendi, WANG Xiaohua, XU Liangfeng, YANG Juan. Facial Expression Recognition Based on Local Texture and Shape Features[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1338-1344. doi: 10.11999/JEIT170799

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Facial Expression Recognition Based on Local Texture and Shape Features

doi: 10.11999/JEIT170799

1.
2.
(School of Computer and Information of Hefei University of Technology, Hefei 230009, China)

Funds:

The National Natural Science Foundation of China (61672202, 61432004, 61502141), The National Natural Science Foundation of China-Shenzhen Joint Foundation (Key Project) (U1613217), The Key University Science Research Project of Anhui Province (KJ2017A368)

Received Date: 2017-08-07
Rev Recd Date: 2017-12-28
Publish Date: 2018-06-19

Abstract

Abstract

In order to improve the inadequacies of Local Binary Pattern (LBP), Center-Symmetric Local Binary Pattern (CS-LBP) and Histogram of Oriented Gradient (HOG) algorithm, Center-Symmetric Local Smooth Binary Pattern (CS-LSBP) and Histogram of Oriented Absolute Gradient (HOAG) are proposed, and a facial expression recognition method based on local texture and local shape features is proposed in this paper. Firstly, CS-LSBP and HOAG are used to extract two local features of expression image of the face. Then, Canonical Correlation Analysis (CCA) is used to fuse two local features. Finally, Support Vector Machine (SVM) is performed for the expression classification. Experimental results on JAFFE and Cohn-Kanade (CK) facial expression databases show that, the improved feature extraction method can extract the detail information of the image more completely and accurately. And the fusion method based on CCA can give full play to the representation ability of each feature. The facial expression recognition method proposed in this paper obtains a better recognition effect.

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References(26)

References

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