Micro-expression recognition method based on CNN-LSTM hybrid network Online publication date: Tue, 13-Sep-2022
by Wang Qingqing
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 23, No. 1, 2022
Abstract: Micro-expression has the characteristics of short duration and weak intensity, which makes it difficult to recognise. This paper is mainly to study the recognition of micro-expressions. Combined with the deep learning method, a hybrid neural network model based on CNN-LSTM is proposed, which mainly includes two parts. In one part, convolution neural network is used to extract micro-expression features. In the other part, LSTM correlation model is used to extract the information hidden in the time domain of micro-expression, which makes up for the lack of dynamic features extracted by CNN model. The proposed algorithm is tested on CASMEII data set. The experimental results show that the text accuracy of this algorithm reached 74.8%. It can be seen that the model can make full use of the information of features in the time domain, so as to more effectively carry out micro-expression recognition, and has more advantages than traditional methods.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Wireless and Mobile Computing (IJWMC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com