| WU Jin(吴进),SHI Qianwen,XI Meng,WANG Lei,ZENG Huadie.[J].高技术通讯(英文),2022,28(1):63~71 |
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| An improved micro-expression recognition algorithm of 3D convolutional neural network |
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| DOI：10.3772/j.issn.1006-6748.2022.01.008 |
| 中文关键词: |
| 英文关键词: micro-expression recognition, deep learning, three-dimensional convolutional neural network (3D-CNN), batch normalization (BN) 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) | | SHI Qianwen | (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) | | WANG Lei | (School of Electronic and Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, P.R.China) | | ZENG Huadie | (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: |
| The micro-expression lasts for a very short time and the intensity is very subtle. Aiming at the problem of its low recognition rate, this paper proposes a new micro-expression recognition algorithm based on a three-dimensional convolutional neural network (3D-CNN), which can extract two-dimensional features in spatial domain and one-dimensional features in time domain, simultaneously. The network structure design is based on the deep learning framework Keras, and the discarding method and batch normalization (BN) algorithm are effectively combined with three-dimensional visual geometry group block (3D-VGG-Block) to reduce the risk of overfitting while improving training speed. Aiming at the problem of the lack of samples in the data set, two methods of image flipping and small amplitude flipping are used for data amplification. Finally, the recognition rate on the data set is as high as 69.11%. Compared with the current international average micro-expression recognition rate of about 67%, the proposed algorithm has obvious advantages in recognition rate. |
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