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Facial expression recognition via learning deep sparse autoencoders
- Source :
- Neurocomputing. 273:643-649
- Publication Year :
- 2018
- Publisher :
- Elsevier BV, 2018.
-
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.
- Subjects :
- 0209 industrial biotechnology
Facial expression
Face hallucination
business.industry
Computer science
Cognitive Neuroscience
Speech recognition
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Pattern recognition
02 engineering and technology
Field (computer science)
Computer Science Applications
ComputingMethodologies_PATTERNRECOGNITION
020901 industrial engineering & automation
Facial expression recognition
Discriminative model
Artificial Intelligence
Pattern recognition (psychology)
0202 electrical engineering, electronic engineering, information engineering
Feature (machine learning)
Three-dimensional face recognition
020201 artificial intelligence & image processing
Artificial intelligence
business
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 273
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........79453e2019ccdc0e4432e2b20a2d7eab