Facial expression recognition based on fusion of extended LDP and Gabor features

doi:10.19682/j.cnki.1005-8885.2018.0005

中国邮电高校学报(英文) ›› 2018, Vol. 25 ›› Issue (1): 48-53.doi: 10.19682/j.cnki.1005-8885.2018.0005

• Artificial Intelligence • 上一篇下一篇

Facial expression recognition based on fusion of extended LDP and Gabor features

罗元¹,余朝靖²,张毅¹,王薄宇¹

1. 重庆邮电大学
2. 信息无障碍研发中心

收稿日期:2017-06-14 修回日期:2018-01-17 出版日期:2018-02-28 发布日期:2018-02-28
通讯作者: 余朝靖 E-mail:1027096592@qq.com
基金资助:
重庆市科学技术委员会项目

Facial expression recognition based on fusion of extended LDP and Gabor features

Received:2017-06-14 Revised:2018-01-17 Online:2018-02-28 Published:2018-02-28
Contact: Chao-Jing YU E-mail:1027096592@qq.com

摘要/Abstract

摘要： The local direction pattern (LDP) is unsusceptible to random noise which is widely used in texture extraction of face region. LDP cannot encode the central pixel thus the important information will be lost. Thus a new feature descriptor called extended local directional pattern (ELDP) is proposed for face extraction. First, the mean value of the eight directional edge response values and the gray value of center pixel are calculated. Second, the mean value is taken as the threshold. Then, the expression image is encoded using nine encoded values. In order to reduce redundant information and get more effective information, the Gabor filter is used to obtain the multi-direction Gabor magnitude maps (GMMs), and then the ELDP is used to encode the GMMs. Finally, support vector machine (SVM) is applied to classify and recognize facial expression. The experimental results show that the feature dimensions is greatly reduced and the rate of facial expression recognition is improved.

关键词: Gabor变换

Abstract: The local direction pattern (LDP) is unsusceptible to random noise which is widely used in texture extraction of face region. LDP cannot encode the central pixel thus the important information will be lost. Thus a new feature descriptor called extended local directional pattern (ELDP) is proposed for face extraction. First, the mean value of the eight directional edge response values and the gray value of center pixel are calculated. Second, the mean value is taken as the threshold. Then, the expression image is encoded using nine encoded values. In order to reduce redundant information and get more effective information, the Gabor filter is used to obtain the multi-direction Gabor magnitude maps (GMMs), and then the ELDP is used to encode the GMMs. Finally, support vector machine (SVM) is applied to classify and recognize facial expression. The experimental results show that the feature dimensions is greatly reduced and the rate of facial expression recognition is improved.

Key words: facial expression recognition, local direction pattern, ELDP, Gabor

参考文献

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Facial expression recognition based on fusion of extended LDP and Gabor features

Facial expression recognition based on fusion of extended LDP and Gabor features

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