Deep Belief Network and Auto-Encoder for Face Classification

TitleDeep Belief Network and Auto-Encoder for Face Classification
Publication TypeJournal Article
Year of Publication2019
AuthorsBouchra, N., A. Aouatif, N. Mohammed, and H. Nabil
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
IssueRegular Issue
Date Published06/2019

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.

KeywordsDeep Belief Network, Deep Learning, Facial recognition, Neural Network, Stacked Auto-Encoder
ijimai_5_5_3.pdf1.13 MB