eprintid: 7260
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/72/60
datestamp: 2023-11-09 16:19:03
lastmod: 2023-11-09 16:19:03
status_changed: 2023-11-09 16:08:54
type: conference_item
metadata_visibility: show
creators_name: Selamat, M.H.
creators_name: Md Rais, H.
title: Image face recognition using Hybrid Multiclass SVM (HM-SVM)
ispublished: pub
keywords: Decision trees; Extraction; Feature extraction; Image processing; Information science; Principal component analysis; Radial basis function networks; Support vector machines; Vectors; Video signal processing, Accuracy evaluation; Dimension reduction; Face recognition technique; Image and video processing; Multi-class support vector machines; Polynomial kernels; Principle component analysis; Radial basis function kernels, Face recognition
note: cited By 14; Conference of International Conference on Computer, Control, Informatics and Its Applications, IC3INA 2015 ; Conference Date: 5 October 2015 Through 7 October 2015; Conference Code:118992
abstract: Face recognition was one of the most popular topics in the image and video processing research fields. It became main attraction by many researcher due to demand from commercial and law enforcement sectors. The main issue in face recognition are application sensitivity toward intrinsic factors and extrinsic factors. Beside, computation time and memory usage are the important aspect to been consider. This research, introduced a hybrid face recognition technique, which consist of feature extraction and Multiclass Support Vector Machine (M-SVM) classifier. In the first part, Principal Component Analysis (PCA) was used for image dimension reduction and feature extraction. Then two Multiclass Support Vector Machine (M-SVM) strategies were utilized to tackle the face recognition problem. Cambridge ORL Face Database was used which consist of 400 images of 40 individuals. The accuracy evaluation of this research was based on two different SVM kernel types. Comparison was made to classic one-versus-one and bottom-up decision tree Multiclass Support Vector Machine. As a result, the proposed algorithm shown a consistent and higher accuracy rather than classic strategies approximately by 4.5-18.1 using polynomial kernel and 4.4-20.8 by using radial basis function kernel. © 2015 IEEE.
date: 2016
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963657134&doi=10.1109%2fIC3INA.2015.7377765&partnerID=40&md5=1583123833eb20b1878b8b483613f6f5
id_number: 10.1109/IC3INA.2015.7377765
full_text_status: none
publication: Proceeding - 2015 International Conference on Computer, Control, Informatics and Its Applications: Emerging Trends in the Era of Internet of Things, IC3INA 2015
pagerange: 159-164
refereed: TRUE
isbn: 9781479987733
citation:   Selamat, M.H. and Md Rais, H.  (2016) Image face recognition using Hybrid Multiclass SVM (HM-SVM).  In: UNSPECIFIED.