摘要
针对人脸表情识别背景复杂性以及表情识别的鲁棒性问题,基于Dempster-Shafer(DS)证据理论,提出了一种融合主动形状模型(ASM)差分纹理特征和局部方向模式(LDP)特征的人脸表情识别方法。ASM差分纹理既能有效地屏蔽个体人脸之间的差异,又能保留人脸表情信息。LDP特征通过计算8个方向的边缘响应来对图像进行编码,因此具有很强的抗噪能力,能够捕捉人脸因表情而产生的细微变化。在DS证据理论融合时,针对不同的特征对表情的识别率,分别用不同的权重系数来计算概率分配值。通过对JAFFE和Cohn-Kanade混合数据库进行实验,表情识别的平均识别率为97.08%,比单特征LDP高出一个百分点,有效地提高了表情识别率和鲁棒性。
To solve the problem of complex background and robustness in facial expression recognition, a novel method for facial expression recognition was proposed, which combined Active Shape Model （ASM） differential texture features and Local Directional Pattern （LDP） features in decision-making level by Dempster-Shafer （DS） evidence theory. ASM differential texture features could shield the differences between individuals effectively. Meanwhile it could try to retain expression information. LDP is a robust feature descriptor, which computes the edge response values in different directions and used these to encode the image texture. So LDP features have strong anti-noise capability and can capture the subtle changes caused by facial expression. With the consideration of different expression recognition rates for different features, different weight coefficients were selected to calculate probability assignment value during the process of DS evidence fusion. By conducting the experiments on JAFFE database and Cohn-Kanade database, the average recognition of facial expression can reach to 97.08% and is 1% higher than the method using single LDP feature. The experimental results show that the recognition rate and the robustness of facial expression are promoted.
出处
《计算机应用》
CSCD
北大核心
2015年第3期783-786,796,共5页
journal of Computer Applications
基金
广西自然科学基金资助项目(2013GXNSFBA019278)
广西高等学校科研项目(2013YB032)
广西师范大学博士启动基金资助项目
药用资源化学与药物分子工程教育部重点实验室资助课题(CMEMR2014-B15)
广西自动检测技术与仪器重点实验室基金资助项目(YQ14202)
关键词
主动形状模型
差分纹理
局部方向模式
DS证据理论
Active Shape Model （ASM）
differential texture
Local Directional Pattern （LDP）
Dempster-Shafer （DS）evidence theory
