01713nas a2200253   4500000000100000000000100001008004100002260001200043653002300055653001900078653001800097653002400115653002500139100001900164700001900183700001600202700001800218245006500236856009600301300001000397490000600407520103200413022001401445       2019                            d  c06/201910aFacial recognition10aNeural Network10aDeep Learning10aDeep Belief Network10aStacked Auto-Encoder1 aNassih Bouchra1 aNgadi Mohammed1 aHmina Nabil1 aAmine Aouatif00aDeep Belief Network and Auto-Encoder for Face Classification  uhttps://www.ijimai.org/journal/sites/default/files/files/2018/06/ijimai_5_5_3_pdf_64780.pdf  a22-290 v53 aThe 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.  a1989-1660