摘要
人类面部表情是其心理情绪变化的最直观刻画,不同人的面部表情具有很大差异,现有表情识别方法均利用面部统计特征区分不同表情,其缺乏对于人脸细节信息的深度挖掘。根据心理学家对面部行为编码的定义可以看出,人脸的局部细节信息决定了其表情意义。因此该文提出一种基于多尺度细节增强的面部表情识别方法,针对面部表情受图像细节影响较大的特点,提出利用高斯金字塔提取图像细节信息,并对图像进行细节增强,从而强化人脸表情信息。针对面部表情的局部性特点,提出利用层次结构的局部梯度特征计算方法,描述面部特征点局部形状特征。最后,使用支持向量机(SVM)对面部表情进行分类。该文在CK+表情数据库中的实验结果表明,该方法不仅验证了图像细节对面部表情识别过程的重要作用,而且在小规模训练数据下也能够得到非常好的识别结果,表情平均识别率达到98.19%。
Facial expression is the most intuitive description of changes in psychological emotions,and different people have great differences in facial expressions.The existing facial expression recognition methods use facial statistical features to distinguish among different expressions,but these methods are short of deep exploration for facial detail information.According to the definition of facial behavior coding by psychologists,it can be seen that the local detail information of the face determines the meaning of facial expression.Therefore,a facial expression recognition method based on multi-scale detail enhancement is proposed,because facial expression is much more affected by the image details than other information,the method proposed in this paper extracts the image detail information with the Gaussian pyramid firstly,thus the image is enhanced in detail to enrich the facial expression information.Secondly,for the local characteristics of facial expressions,a local gradient feature calculation method is proposed based on hierarchical structure to describe the local shape features of facial feature points. Finally, facial expressions are classified using a Support Vector Machine (SVM). Theexperimental results in the CK+ expression database show that the method not only proves the important roleof image detail in facial expression recognition, but also obtains very good recognition results under small-scaletraining data. The average recognition rate of expressions reaches 98.19%.
作者
谭小慧
李昭伟
樊亚春
TAN Xiaohui;LI Zhaowei;FAN Yachun(College of Information Engineering,Capital Normal University,Beijing 100048,China;College of Information Science and Technology,Beijing Normal University,Beijing 100875,China;Beijing Key Laboratory of Electronic System Reliability and Prognostics,Capital Normal University,Beijing 100048,China;Beijing Advanced Innovation Center for Imaging Technology,Capital Normal University,Beijing 100048,China)
出处
《电子与信息学报》
EI
CSCD
北大核心
2019年第11期2752-2759,共8页
Journal of Electronics & Information Technology
基金
国家重点研发计划项目(2017YFB1002804)
国家自然科学基金项目(61602324)
浙江大学CAD&CG国家重点实验室开放课题(A1914)~~
关键词
表情识别
图像金字塔
高斯差分
细节增强
支持向量机
Expression recognition
Image pyramid
Gauss difference
Detail enhancement
Support Vector Machine(SVM)
