| Wu Jin (吴进),Xi Meng,Dai Wei,Wang Lei,Wang Xinran.[J].高技术通讯(英文),2021,27(3):303~309 |
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| Micro-expression recognition algorithm based on the combination of spatial and temporal domains |
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| DOI： |
| 中文关键词: |
| 英文关键词: micro-expression recognition, convolutional neural network (CNN), long short-term memory (LSTM), batch normalization algorithm, dropout |
| 基金项目: |
| Author Name | Affiliation | | Wu Jin (吴进) | (School of Electronic and Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, P.R.China) | | Xi Meng | (School of Electronic and Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, P.R.China) | | Dai Wei | (School of Electronic and Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, P.R.China) | | Wang Lei | (School of Electronic and Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, P.R.China) | | Wang Xinran | (School of Electronic and Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, P.R.China) |
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| 中文摘要: |
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| 英文摘要: |
| Aiming at the problem of unsatisfactory effects of traditional micro-expression recognition algorithms, an efficient micro-expression recognition algorithm is proposed, which uses convolutional neural networks (CNN) to extract spatial features of micro-expressions, and long short-term memory network (LSTM) to extract time domain features. CNN and LSTM are combined as the basis of micro-expression recognition. In many CNN structures, the visual geometry group (VGG) using a small convolution kernel is finally selected as the pre-network through comparison. Due to the difficulty of deep learning training and over-fitting, the dropout method and batch normalization method are used to solve the problem in the VGG network. Two data sets CASME and CASME II are used for test comparison, in order to solve the problem of insufficient data sets, randomly determine the starting frame, and a fixed-length frame sequence is used as the standard, and repeatedly read all sample frames of the entire data set to achieve traversal and data amplification. Finally, a high recognition rate of 67.48% is achieved. |
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