摘要
微表情顶点帧蕴含着丰富的微表情信息,为了准确地检测出微表情顶点帧,本文提出了一种基于光流特征的神经网络分类,并利用先验知识规则进行取舍的检测方法.该方法针对固定滑窗大小内的图像进行光流信息提取,利用双输入特征提取网络对x,y方向的光流信息进行时空特征提取,并进行分类,经根据微表情先验知识所设计的取舍规则后处理后,改善了检测准确度.实验结果表明,在数据集CASMEⅡ上测试,顶点定位率(apex spotting rate,ASR)指标达到了0.945,F_(1)-score指标达到了0.925.
Micro-expression apex frame contains abundant micro-expression information.In order to spot the apex frame accurately,a neural network classification was proposed based on optical flow characteristics.Taking prior knowledge as rules,a detection method was designed to realize micro-expression apex frame spotting.Firstly,optical flow information was extracted from the image in a fixed size sliding window.And then,the spatial and temporal features of optical flow information in x and y directions was extracted and classified based on dual input network.Finally,according to the trade-off rules based on prior knowledge of micro expression,a post-processing was carried out to improve the detection accuracy.The experimental results on data set CASMEⅡtesting show that the apex spotting rate(ASR)and F_(1)-score can reach up to 0.945 and 0.925 respectively.
作者
郑戍华
陈梦心
王向周
弓雪雅
ZHENG Shuhua;CHEN Mengxin;WANG Xiangzhou;GONG Xueya(School of Automation,Beijing Institute of Technology,Beijing 100081,China)
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2022年第7期749-754,共6页
Transactions of Beijing Institute of Technology
基金
国家部委预研资助项目(5200-2020036147A-0-0-00)。
关键词
微表情顶点帧
双输入网络
分类后处理
micro-expression apex frame
dual input network
classification post processing
