New Proposed Fusion between DCT for Feature Extraction and NSVC for Face Classification

B. Nassih; M. Ngadi; A. Amine; A. El-Attar

doi:10.2478/cait-2018-0030

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New Proposed Fusion between DCT for Feature Extraction and NSVC for Face Classification

Cybernetics and Information Technologies

Volume 18 (2018): Issue 2 (June 2018)

By: B. Nassih, M. Ngadi, A. Amine and A. El-Attar

Open Access

|Jun 2018

Abstract

Feature extraction is an interactive and iterative analysis process of a large dataset of raw data in order to extract meaningful knowledge. In this article, we present a strong descriptor based on the Discrete Cosine Transform (DCT), we show that the new DCT-based Neighboring Support Vector Classifier (DCT-NSVC) provides a better results compared to other algorithms for supervised classification. Experiments on our real dataset named BOSS, show that the accuracy of classification has reached 99%. The application of DCT-NSVC on MIT-CBCL dataset confirms the performance of the proposed approach.

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Articles in this issue

DOI: https://doi.org/10.2478/cait-2018-0030 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702

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Language: English

Page range: 89 - 97

Submitted on: Aug 15, 2017

Accepted on: Apr 2, 2018

Published on: Jun 30, 2018

Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

Supervised learning,

DCT,

NSVC,

shape recognition,

SVM,

feature extraction

Related subjects:

Computer sciences,

Information technology

© 2018 B. Nassih, M. Ngadi, A. Amine, A. El-Attar, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Volume 18 (2018): Issue 2 (June 2018)