Facial expression recognition based on convolutional block attention module and multi-feature fusion
by Man Jiang; Shoulin Yin
International Journal of Computational Vision and Robotics (IJCVR), Vol. 13, No. 1, 2023

Abstract: In this paper, we focus on the research of facial expression recognition. A novel convolutional block attention module and multi-feature fusion method are proposed for facial expression recognition. The local feature clustering loss function is proposed, which can reduce the difference between the same classes of images and enlarge the difference between different classes of images in the training process. The convolutional block attention module is adopted to better express facial expressions in local areas with rich expressions. Experimental results show that the proposed method can effectively recognise different expressions on the RAF dataset and CK+ dataset compared with other state-of-the-art methods.

Online publication date: Wed, 30-Nov-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
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 Computational Vision and Robotics (IJCVR):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your 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