Open Access Open Access  Restricted Access Subscription or Fee Access

Non-Linear Dimensional Reduction Based Deep Learning Algorithm for Face Classification

B. Nassih, A. Amine, M. Ngadi, N. Hmina

Abstract



Deep learning is one of a powerful new field in Artificial intelligent, in this paper, we exhibit a technique of Autoencoder via a deep learning for Dimensionality reduction, applied on face classification. First, the Autoencoder is used to extract face features. Second, SVM kernel
functions as Linear-SVM, Polynomial-SVM, Sigmoid-SVM, and Rbf-SVM are applied. Further, experiments are conducted to compare the performance of Autoencoder with two methods of dimensionality reduction which are Principal Component Analysis (PCA) and Multi-Dimensional Scaling (MDS) in order to evaluate the effectiveness of Autoencoder with the four kernel functions for face classification. Moreover, the aim of this work is to get rid of the redundant features via Autoencoder, at the same time, make a comparative study between Deep Learning method and the two previous methods, in order to test the suitability of the first one with SVM kernel functions. we evaluate the performance of our contribution using two face databases. The first one is our own created face database with hard lighting conditions named BOSS. The second one is MIT-CBCL database. The results indicate
the robustness of the new proposed approach with the four kernel functions for face classification.

Keywords


face classification, Dimensionality reduction, Autoencoder, SVM

Full Text:

PDF


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.