Deep Belief Network and Auto-Encoder for Face Classification

Deep Belief Network and Auto-Encoder for Face Classification

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 initia...

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Journal Title: International Journal of Interactive Multimedia and Artificial Intelligence
First author: Nassih Bouchra
Other Authors: Amine Aouatif;
Ngadi Mohammed;
Hmina Nabil
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Language: Undetermined
Get full text: https://www.ijimai.org/journal/sites/default/files/files/2018/06/ijimai_5_5_3_pdf_64780.pdf
https://www.ijimai.org/journal/node/2444
Resource type: Journal Article
Source: International Journal of Interactive Multimedia and Artificial Intelligence; Vol 5, No 5 (Year 2019).
DOI:
Publisher: Universidad Internacional de La Rioja
Usage rights: Reconocimiento (by)
Subjects: Physical/Engineering Sciences --> Computer Science, Artificial Intelligence
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.