高婷婷 李航 殷守林

摘 要：针对人脸表情识别领域受噪声和遮挡等因素影响识别率不高的问题，结合局部和全局特征，提出一种基于面部表情的情感分析混合方法。首先，通过将梯度直方图（HOG）与复合局部三元模式（C-LTP）融合来进行特征提取;其次，将HOG和C-LTP提取的特征融合到单个特征向量中;最后，采用多类支持向量机分类器把特征向量进行情感分类;最后，将提出的方法在3个公共表情图像数据库中与现有的表情识别方法进行对比实验。结果表明，提出的方法在MMI，JAFFE，CK+数据库上的正确识别率分别为98.28%，95.75%，99.64%，平均识别率比其他方法高出10%，优于其他现有的方法。提出的表情识别方法，可有效促进人机交互系统的发展和计算机图像理解的研究，对实现人体语言与自然语言的融合，以及语言与表情连接模型的建立与实现具有重要意义。

关键词：模式识别;人脸表情识别;特征融合;HOG;C-LTP;支持向量机

中图分类号：TP957.52 文献标识码：A

doi：10.7535/hbkd.2021yx02004

A facial expression recognition method based on face texture feature fusion

GAO Tingting，LI Hang，YIN Shoulin

（Software College，Shenyang Normal University，Shenyang，Liaoning 110034，China）

Abstract：Aiming at facial expression recognition， the recognition rate is not high due to noise and occlusion. A hybrid approach of facial expression has been presented by combining local and global features. First， feature extraction is performed to fuse the histogram of oriented gradients （HOG） descriptor with the compounded local ternary pattern （C-LTP） descriptor. Second， features extracted by HOG and C-LTP are fused into a single feature vector. Third， the feature vector is sent to a multi-class support vector machine classifier for facial classification. Finally， the proposed method is compared with the existing facial expression recognition methods in three public facial expression image databases， and the results show that the recognition rates of the proposed method in MMI， JAFFE and CK+ databases are 98.28%， 95.75% and 99.64%， respectively. The average recognition rate is 10% higher than other methods， which is better than other existing methods. The results of this study provide a reference for the research of facial expression recognition in many situations. The method of facial expression recognition proposed can effectively promote the development of human-computer interaction system and the study of computer image understanding. It is of great significance to realize the fusion of human language and natural language， as well as the establishment and implementation of the connection model between language and expression.

Keywords：

pattern recognition; facial expression recognition; feature fusion; HOG; C-LTP; support vector machine

面部表情[1]是人際关系中非常重要的交流方式。人脸表情识别在测谎、行为分析、监视系统、运输和机器人技术等多个研究和开发领域中具有多种应用[2-3]。随着机器人的发展，表情识别将有助于在人与机器之间创建智能的视觉界面，从而促进人机交互（HCI）[4]。

此外，在许多现实工作中，例如，驾驶员疲劳检测、教师情绪检测等，都需要高效的人脸表情识别。目前，基于深度学习方法已被用于识别面部表情。李军等[5]提出了一种融合多尺度卷积神经网络和双向长短期记忆的模型，不仅能够增强特征信息间的联系，还可通过不同尺度的卷积核提取到更加丰富的特征信息。……