Paper Info
Title
Facial expression recognition via learning deep sparse autoencoders.
Abstract
Facial expression recognition is an important research issue in the pattern recognition field. In this paper, we intend to present a novel framework for facial expression recognition to automatically distinguish the expressions with high accuracy. Especially, a high-dimensional feature composed by the combination of the facial geometric and appearance features is introduced to the facial expression recognition due to its containing the accurate and comprehensive information of emotions. Furthermore, the deep sparse autoencoders (DSAE) are established to recognize the facial expressions with high accuracy by learning robust and discriminative features from the data. The experiment results indicate that the presented framework can achieve a high recognition accuracy of 95.79% on the extended Cohn–Kanade (CK+) database for seven facial expressions, which outperforms the other three state-of-the-art methods by as much as 3.17%, 4.09% and 7.41%, respectively. In particular, the presented approach is also applied to recognize eight facial expressions (including the neutral) and it provides a satisfactory recognition accuracy, which successfully demonstrates the feasibility and effectiveness of the approach in this paper.
Year
DOI
Venue
2018
10.1016/j.neucom.2017.08.043
Neurocomputing
Keywords
Field
DocType
Facial expression recognition,Sparse autoencoders,Deep architecture,High-dimensional feature,Histogram of oriented gradients (HOG)
Face hallucination,Pattern recognition,Facial expression recognition,Three-dimensional face recognition,Expression (mathematics),Computer science,Speech recognition,Facial expression,Artificial intelligence,Discriminative model
Journal
Volume
ISSN
Citations 
273
0925-2312
67
PageRank 
References 
Authors
1.46
35
6
Name
Order
Citations
PageRank
Nianyin Zeng138412.14
Hong Zhang227626.98
Baoye Song3712.58
Weibo Liu452016.88
Yurong Li523416.14
Abdullah M. Dobaie61197.75