Enhanced Forest Microexpression Recognition Based on Optical Flow Direction Histogram and Deep Multiview Network
In order to recognize the instantaneous changes of facial microexpressions in natural environment, a method based on optical flow direction histogram and depth multiview network to enhance forest microexpression recognition was proposed. In the preprocessing stage, the histogram equalization of the...
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doaj-ad59bf07221844aab0aba5457b63608f2020-11-25T03:47:55ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472020-01-01202010.1155/2020/56759145675914Enhanced Forest Microexpression Recognition Based on Optical Flow Direction Histogram and Deep Multiview NetworkHuanmin Wang0Mechatronics T&R Institute, Lanzhou Jiaotong University, Lanzhou 730070, ChinaIn order to recognize the instantaneous changes of facial microexpressions in natural environment, a method based on optical flow direction histogram and depth multiview network to enhance forest microexpression recognition was proposed. In the preprocessing stage, the histogram equalization of the acquired face image is performed, and then the dense key points of the face are detected. According to the coordinates of the key points and the face action coding system (FACS), the face region is divided into 15 regions of interest (ROI). In the feature extraction stage, the optical flow direction histogram feature between adjacent frames in ROI is extracted to detect the peak frame of microexpression sequence. Finally, the average optical flow direction histogram feature of the image sequence from the initial frame to the peak frame is extracted. In the classification stage, firstly, the head pose parameters under horizontal degrees of freedom are estimated to eliminate the influence of head pose motion, and a forest multiview conditional probability model based on deep multiview network is established. Conditional probability and neural connection function are introduced into the node splitting learning of random tree to improve the learning ability and distinguishing ability of the model on the limited training set. Finally, multiview-weighted voting is used to determine the categories of facial microexpressions. Experiments on CASME II microexpression dataset show that the proposed method can effectively describe the changes of microexpressions and improve the recognition accuracy compared with other new methods.http://dx.doi.org/10.1155/2020/5675914 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Huanmin Wang |
spellingShingle |
Huanmin Wang Enhanced Forest Microexpression Recognition Based on Optical Flow Direction Histogram and Deep Multiview Network Mathematical Problems in Engineering |
author_facet |
Huanmin Wang |
author_sort |
Huanmin Wang |
title |
Enhanced Forest Microexpression Recognition Based on Optical Flow Direction Histogram and Deep Multiview Network |
title_short |
Enhanced Forest Microexpression Recognition Based on Optical Flow Direction Histogram and Deep Multiview Network |
title_full |
Enhanced Forest Microexpression Recognition Based on Optical Flow Direction Histogram and Deep Multiview Network |
title_fullStr |
Enhanced Forest Microexpression Recognition Based on Optical Flow Direction Histogram and Deep Multiview Network |
title_full_unstemmed |
Enhanced Forest Microexpression Recognition Based on Optical Flow Direction Histogram and Deep Multiview Network |
title_sort |
enhanced forest microexpression recognition based on optical flow direction histogram and deep multiview network |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2020-01-01 |
description |
In order to recognize the instantaneous changes of facial microexpressions in natural environment, a method based on optical flow direction histogram and depth multiview network to enhance forest microexpression recognition was proposed. In the preprocessing stage, the histogram equalization of the acquired face image is performed, and then the dense key points of the face are detected. According to the coordinates of the key points and the face action coding system (FACS), the face region is divided into 15 regions of interest (ROI). In the feature extraction stage, the optical flow direction histogram feature between adjacent frames in ROI is extracted to detect the peak frame of microexpression sequence. Finally, the average optical flow direction histogram feature of the image sequence from the initial frame to the peak frame is extracted. In the classification stage, firstly, the head pose parameters under horizontal degrees of freedom are estimated to eliminate the influence of head pose motion, and a forest multiview conditional probability model based on deep multiview network is established. Conditional probability and neural connection function are introduced into the node splitting learning of random tree to improve the learning ability and distinguishing ability of the model on the limited training set. Finally, multiview-weighted voting is used to determine the categories of facial microexpressions. Experiments on CASME II microexpression dataset show that the proposed method can effectively describe the changes of microexpressions and improve the recognition accuracy compared with other new methods. |
url |
http://dx.doi.org/10.1155/2020/5675914 |
work_keys_str_mv |
AT huanminwang enhancedforestmicroexpressionrecognitionbasedonopticalflowdirectionhistogramanddeepmultiviewnetwork |
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