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 ( 3 D-CNN ) , which can extract two-di-mensional 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 vis-ual 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 recog-nition rate of about 67％, the proposed algorithm has obvious advantages in recognition rate.

WU Jin;SHI Qianwen;XI Meng;WANG Lei;ZENG Huadie

School of Electronic and Engineering,Xi'an University of Posts and Telecommunications,Xi'an 710121,P.R.China

高技术通讯（英文版）

[1]WU Jin;SHI Qianwen;XI Meng;WANG Lei;ZENG Huadie-.An improved micro-expression recognition algorithm of 3D convolutional neural network)[J].高技术通讯（英文版）,2022(01):63-71

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