TY - JOUR
KW - Facial recognition
KW - Neural Network
KW - Deep Learning
KW - Deep Belief Network
KW - Stacked Auto-Encoder
AU - Nassih Bouchra
AU - Ngadi Mohammed
AU - Hmina Nabil
AU - Amine Aouatif
AB - 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.
IS - Regular Issue
M1 - 5
N2 - 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.
PY - 2019
SP - 22
EP - 29
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Deep Belief Network and Auto-Encoder for Face Classification
UR - https://www.ijimai.org/journal/sites/default/files/files/2018/06/ijimai_5_5_3_pdf_64780.pdf
VL - 5
SN - 1989-1660
ER -