电子测量与仪器学报

吴昊,胡敏,高永,王晓华,黄忠.融合DCLBP和HOAG特征的人脸表情识别方法[J].电子测量与仪器学报,2020,34(2):73-79

融合DCLBP和HOAG特征的人脸表情识别方法

Facial expression recognition based on DCLBP and HOAG

DOI：

中文关键词: 人脸表情识别双编码局部二值模式绝对梯度方向直方图典型相关分析

英文关键词:facial expression recognition double coding local binary pattern（DCLBP） histogram of oriented absolute gradient (HOAG) canonical correlation analysis (CCA)

基金项目:国家自然科学基金项目（61672202，61673156）、国家自然科学基金深圳联合基金重点项目（U1613217）资助

作者	单位
吴昊	1.合肥工业大学计算机与信息学院合肥,2.情感计算与先进智能机器安徽省重点实验室合肥
胡敏	1.合肥工业大学计算机与信息学院合肥,2.情感计算与先进智能机器安徽省重点实验室合肥
高永	1.合肥工业大学计算机与信息学院合肥,2.情感计算与先进智能机器安徽省重点实验室合肥
王晓华	1.合肥工业大学计算机与信息学院合肥,2.情感计算与先进智能机器安徽省重点实验室合肥
黄忠	1.合肥工业大学计算机与信息学院,3.安庆师范大学物理与电气工程学院

Author	Institution
Wu Hao	1.School of Computer and Information, Hefei University of Technology,2.Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine
Hu Min	1.School of Computer and Information, Hefei University of Technology,2.Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine
Gao Yong	1.School of Computer and Information, Hefei University of Technology,2.Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine
Wang Xiaohua	1.School of Computer and Information, Hefei University of Technology,2.Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine
Huang Zhong	1.School of Computer and Information, Hefei University of Technology,3.School of Physics and Electronic Engineering, Anqing Normal University

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中文摘要:

为了进一步提高人脸表情识别算法的准确性，提出一种融合双编码局部二值模式(DCLBP)算子和绝对梯度直方图(HOAG)算子的人脸表情识别方法，该方法首先利用DCLBP算子提取人脸图像的局部纹理特征，利用HOAG算子提取人脸图像的局部形状特征；然后，采用典型相关分析法(CCA)融合所提取的两种特征；最后，利用支持向量机(SVM)进行人脸表情分类。实验结果表明，与单一特征识别方法和级联特征识别方法相比，本文方法获得了更好的识别效果，在CK (Cohn Kanade)和JAFFE数据集上的实验分别达到了100%和9905%的识别率，与其他相关方法的比较也验证了其有效性。

英文摘要:

In order to further improve the accuracy of facial expression recognition algorithm, this paper proposes a facial expression recognition method that combines the double coding local binary pattern (DCLBP) operator and the histogram of oriented absolute gradient (HOAG) operator. First, the method uses DCLBP operator to extract the local texture features of the face image and the HOAG operator to extract the local shape features of the face image. Then, the two extracted correlation features are fused by the canonical correlation analysis (CCA). Finally, using the support vector machine (SVM) to classify facial expression. Compared with the single feature recognition method and the cascade feature recognition method, the experimental results show that the proposed method achieves better recognition results, and the recognition rate on the Cohn Kanade (CK) and JAFFE data sets achieves 100% and 9905% respectively, the comparison with other related methods also verified its effectiveness.

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