【摘 要】
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Aiming at the problem of unsatisfactory effects of traditional micro-expression recognition algo-rithms,an efficient micro-expression recognition algorithm is proposed,which uses convolutional neu-ral networks (CNN) to extract spatial features of micro-ex
【机 构】
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School of Electronic and Engineering,Xi’an University of Posts and Telecommunications,Xi’an 710121,P
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Aiming at the problem of unsatisfactory effects of traditional micro-expression recognition algo-rithms,an efficient micro-expression recognition algorithm is proposed,which uses convolutional neu-ral networks (CNN) to extract spatial features of micro-expressions,and long short-term memory net-work (LSTM) to extract time domain features.CNN and LSTM are combined as the basis of micro-ex-pression recognition.In many CNN structures,the visual geometry group (VGG) using a small convo-lution 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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