eprintid: 6508
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/65/08
datestamp: 2023-11-09 16:18:17
lastmod: 2023-11-09 16:18:17
status_changed: 2023-11-09 16:06:56
type: conference_item
metadata_visibility: show
creators_name: Selamat, M.H.
creators_name: Rais, H.M.
title: Enhancement on image face recognition using Hybrid Multiclass SVM (HM-SVM)
ispublished: pub
keywords: Classification (of information); Discrete wavelet transforms; Extraction; Feature extraction; Image compression; Information science; Principal component analysis; Radial basis function networks; Support vector machines; Vectors; Wavelet transforms, Accuracy evaluation; Dimension reduction; Feature extraction and classification; Multi-class support vector machines; Orthogonal wavelets; Principle component analysis; Radial basis function kernels; Research objectives, Face recognition
note: cited By 8; Conference of 3rd International Conference on Computer and Information Sciences, ICCOINS 2016 ; Conference Date: 15 August 2016 Through 17 August 2016; Conference Code:125433
abstract: These day, face recognition became main attraction by many researcher due to demand from commercial and law enforcement sectors. The main issues in face recognition are the sensitivity toward intrinsic factors and extrinsic factors. Beside, computation time and memory usage are the important aspect to been consider. This research objective focus toward enhanced on Hybrid Multiclass Support Vector Machine (HM-SVM) model. The model consist of several phases which are data preparation, feature extraction and classification. In feature extraction phase, Discrete Wavelet Transform (DWT) and Principle Component Analysis (PCA) was applied to perform image compression, dimension reduction and feature extraction. Then, Hybrid Multiclass Support Vector Machine (HM-SVM) strategies were utilized to tackle the face recognition problem. Cambridge ORL Face Database was used as testing and training material. This database consist of 400 images of 40 individuals. The accuracy evaluation of this research was based on two different SVM kernel. Besides, the research was performed based on Orthogonal Wavelet families. Comparison was made with classic Hybrid Multiclass Support Vector Machine (HM-SVM) performance. As a result, the proposed algorithm shown the enhancement improve the classic HS-SVM approximately by 3.13-6.56 using polynomial kernel and 1.88-5.31 by using radial basis function kernel (RBF). © 2016 IEEE.
date: 2016
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010366658&doi=10.1109%2fICCOINS.2016.7783253&partnerID=40&md5=179c196a9bfe7913339067f63418e5a8
id_number: 10.1109/ICCOINS.2016.7783253
full_text_status: none
publication: 2016 3rd International Conference on Computer and Information Sciences, ICCOINS 2016 - Proceedings
pagerange: 424-429
refereed: TRUE
isbn: 9781509051342
citation:   Selamat, M.H. and Rais, H.M.  (2016) Enhancement on image face recognition using Hybrid Multiclass SVM (HM-SVM).  In: UNSPECIFIED.