Deep Belief Network and Auto-Encoder for Face Classification

Author
Keywords
Abstract
The Deep Learning models have drawn ever-increasing research interest owing to their intrinsic capability of overcoming the drawback of traditional algorithm. Hence, we have adopted the representative Deep Learning methods which are Deep Belief Network (DBN) and Stacked Auto-Encoder (SAE), to initialize deep supervised Neural Networks (NN), besides of Back Propagation Neural Networks (BPNN) applied to face classification task. Moreover, our contribution is to extract hierarchical representations of face image based on the Deep Learning models which are: DBN, SAE and BPNN. Then, the extracted feature vectors of each model are used as input of NN classifier. Next, to test our approach and evaluate its performance, a simulation series of experiments were performed on two facial databases: BOSS and MIT. Our proposed approach which is (DBN,NN) has a significant improvement on the classification error rate compared to (SAE,NN) and BPNN which we get 1.14% and 1.96% in terms of error rate with BOSS and MIT respectively.
Year of Publication
2019
Journal
International Journal of Interactive Multimedia and Artificial Intelligence
Volume
5
Issue
Regular Issue
Number
5
Number of Pages
22-29
Date Published
06/2019
ISSN Number
1989-1660
Citation Key
URL
https://www.ijimai.org/journal/sites/default/files/files/2018/06/ijimai_5_5_3_pdf_64780.pdf
DOI
10.9781/ijimai.2018.06.004
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