| 摘 要: 针对现有的人脸表情识别方法易受到光照、噪声等因素的影响导致识别率较低的问题，本文提出了一种 基于特征融合的人脸表情识别方法。从两方面对表情信息进行描述，通过局部二值模式(Local Binary Pattern,LBP)和 词袋模型(Bag Of Words,BOW)分别对表情图像进行纹理特征和语义特征提取，然后将两种特征进行线性融合，最后 使用支持向量机(Support Vector Machine,SVM)进行表情分类识别。本文方法在JAFFE和CK+表情数据集上分别取得 了98.76%和97.58%的识别率，验证了所提出方法的有效性。 |
| 关键词: 人脸表情识别；LBP；BOW；特征融合；SVM |
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中图分类号: TP391
文献标识码: A
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| Research on Facial Expression Recognition Based on Feature Fusion |
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GAO Lixiang,GAO Lei1,2,3
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1.( 1.Tianjin YunkeShixun Technology Co., Ltd., Tianjin 300401, China;2.
2.Tianjin Research and Development Department, Agricultural Bank of China, Tianjin 300000, China;3.
3.School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China)
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| Abstract: This paper proposes a new facial expression recognition method based on feature fusion to solve the problem that the existing facial expression recognition methods are vulnerable to illumination,noise and other factors.The facial expression information is described from two aspects.Local Binary Pattern (LBP) and Bag of Words (BOW) are used to extract texture features and semantic features from facial expression images respectively and then are fused linearly. Finally,the expression is classified and recognized by Support Vector Machine (SVM).The proposed method achieves 98.76% and 97.58% recognition rates on JAFFE and CK + expression datasets respectively,which verifies the effectiveness of the proposed method. |
| Keywords: facial expression recognition;LBP;BOW;feature fusion;SVM |
