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Convolutional neural network for facial expression recognition
KTH, School of Computer Science and Communication (CSC), Media Technology and Interaction Design, MID. Nanjing University of Posts and Telecommunications.
2016 (English)In: Journal of Nanjing University of Posts and Telecommunications, ISSN 1673-5439, Vol. 36, no 1, 16-22 p.Article in journal (Refereed) PublishedText
Abstract [en]

To avoid the complex explicit feature extraction process in traditional expression recognition, a convolutional neural network (CNN) for the facial expression recognition is proposed. Firstly, the facial expression image is normalized and the implicit features are extracted by using the trainable convolution kernel. Then, the maximum pooling is used to reduce the dimensions of the extracted implicit features. Finally, the Softmax classifier is used to classify the facial expressions of the test samples. The experiment is carried out on the CK+ facial expression database using the graphics processing unit (GPU). Experimental results show the performance and the generalization ability of the CNN for facial expression recognition.

Place, publisher, year, edition, pages
Journal of Nanjing Institute of Posts and Telecommunication , 2016. Vol. 36, no 1, 16-22 p.
Keyword [en]
Convolutional neural networks, Deep learning, Facial expression recognition, Feature extraction, Graphics processing unit
National Category
Computer Vision and Robotics (Autonomous Systems) Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:kth:diva-188297DOI: 10.14132/j.cnki.1673-5439.2016.01.003ScopusID: 2-s2.0-84961761164OAI: oai:DiVA.org:kth-188297DiVA: diva2:935418
Note

QC 20160610

Available from: 2016-06-10 Created: 2016-06-09 Last updated: 2016-06-10Bibliographically approved

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Li, Haibo
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Media Technology and Interaction Design, MID
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ReferencesLink to record
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