dc.creatorBouchra, Nassih
dc.creatorMohammed, Ngadi
dc.creatorNabil, Hmina
dc.creatorAouatif, Amine
dc.date.accessioned2022-02-24T09:30:17Z
dc.date.accessioned2023-03-07T19:34:59Z
dc.date.available2022-02-24T09:30:17Z
dc.date.available2023-03-07T19:34:59Z
dc.date.created2022-02-24T09:30:17Z
dc.identifier1989-1660
dc.identifierhttps://reunir.unir.net/handle/123456789/12501
dc.identifierhttp://doi.org/10.9781/ijimai.2018.06.004
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5906798
dc.description.abstractThe 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.
dc.languageeng
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)
dc.relation;vol. 5, nº 5
dc.relationhttps://www.ijimai.org/journal/bibcite/reference/2679
dc.rightsopenAccess
dc.subjectfacial recognition
dc.subjectneural network
dc.subjectdeep learning
dc.subjectdeep belief network
dc.subjectstacked auto-encoder
dc.subjectIJIMAI
dc.titleDeep Belief Network and Auto-Encoder for Face Classification
dc.typearticle


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