A Fusion Face Recognition Approach Based on 7-Layer Deep Learning Neural Network
Joint Authors
Liu, Jianzheng
Fang, Chunlin
Wu, Chao
Source
Journal of Electrical and Computer Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-04-20
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Abstract EN
This paper presents a method for recognizing human faces with facial expression.
In the proposed approach, a motion history image (MHI) is employed to get the features in an expressive face.
The face can be seen as a kind of physiological characteristic of a human and the expressions are behavioral characteristics.
We fused the 2D images of a face and MHIs which were generated from the same face’s image sequences with expression.
Then the fusion features were used to feed a 7-layer deep learning neural network.
The previous 6 layers of the whole network can be seen as an autoencoder network which can reduce the dimension of the fusion features.
The last layer of the network can be seen as a softmax regression; we used it to get the identification decision.
Experimental results demonstrated that our proposed method performs favorably against several state-of-the-art methods.
American Psychological Association (APA)
Liu, Jianzheng& Fang, Chunlin& Wu, Chao. 2016. A Fusion Face Recognition Approach Based on 7-Layer Deep Learning Neural Network. Journal of Electrical and Computer Engineering،Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1108491
Modern Language Association (MLA)
Liu, Jianzheng…[et al.]. A Fusion Face Recognition Approach Based on 7-Layer Deep Learning Neural Network. Journal of Electrical and Computer Engineering No. 2016 (2016), pp.1-7.
https://search.emarefa.net/detail/BIM-1108491
American Medical Association (AMA)
Liu, Jianzheng& Fang, Chunlin& Wu, Chao. A Fusion Face Recognition Approach Based on 7-Layer Deep Learning Neural Network. Journal of Electrical and Computer Engineering. 2016. Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1108491
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1108491