3-dimensional ear recognition based iterative closest point with stochastic clustering matching

Khamiss Masaoud S Algabary, Khairuddin Omar, Md. Jan Nordin

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Ear recognition is a new technology and future trend for personal identification. However, the false detection rate and matching recognition are very challenging due to the ear complex geometry. The Scope of the study is to introduced a combination of Iterative Closest Point (ICP) and Stochastic Clustering Matching (SCM) algorithm for 3D ears matching based on biometrics field with a good steadiness to reduce the false detection rate. The corresponding ear extracts from the side range image and characterized by 3D features. The proposed method used matlab simulation and defined the average detection time 35ms and an identification similarity is 98.25% for the collection of different database. The result shows that the proposed combined method outperforms than the existing of ICP or SCM in terms of detection time and accuracy in training.

Original languageEnglish
Pages (from-to)477-483
Number of pages7
JournalJournal of Computer Science
Volume10
Issue number3
DOIs
Publication statusPublished - 2014

Fingerprint

Biometrics
Geometry

Keywords

  • 3D ears matching
  • Ear identification
  • Iterative closest point (ICP)
  • Preprocessing
  • Stochastic clustering matching (SCM)

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Artificial Intelligence

Cite this

3-dimensional ear recognition based iterative closest point with stochastic clustering matching. / Algabary, Khamiss Masaoud S; Omar, Khairuddin; Nordin, Md. Jan.

In: Journal of Computer Science, Vol. 10, No. 3, 2014, p. 477-483.

Research output: Contribution to journalArticle

@article{f78e9697d3b64bffaa73770c2ad5c9df,
title = "3-dimensional ear recognition based iterative closest point with stochastic clustering matching",
abstract = "Ear recognition is a new technology and future trend for personal identification. However, the false detection rate and matching recognition are very challenging due to the ear complex geometry. The Scope of the study is to introduced a combination of Iterative Closest Point (ICP) and Stochastic Clustering Matching (SCM) algorithm for 3D ears matching based on biometrics field with a good steadiness to reduce the false detection rate. The corresponding ear extracts from the side range image and characterized by 3D features. The proposed method used matlab simulation and defined the average detection time 35ms and an identification similarity is 98.25{\%} for the collection of different database. The result shows that the proposed combined method outperforms than the existing of ICP or SCM in terms of detection time and accuracy in training.",
keywords = "3D ears matching, Ear identification, Iterative closest point (ICP), Preprocessing, Stochastic clustering matching (SCM)",
author = "Algabary, {Khamiss Masaoud S} and Khairuddin Omar and Nordin, {Md. Jan}",
year = "2014",
doi = "10.3844/jcssp.2014.477.483",
language = "English",
volume = "10",
pages = "477--483",
journal = "Journal of Computer Science",
issn = "1549-3636",
publisher = "Science Publications",
number = "3",

}

TY - JOUR

T1 - 3-dimensional ear recognition based iterative closest point with stochastic clustering matching

AU - Algabary, Khamiss Masaoud S

AU - Omar, Khairuddin

AU - Nordin, Md. Jan

PY - 2014

Y1 - 2014

N2 - Ear recognition is a new technology and future trend for personal identification. However, the false detection rate and matching recognition are very challenging due to the ear complex geometry. The Scope of the study is to introduced a combination of Iterative Closest Point (ICP) and Stochastic Clustering Matching (SCM) algorithm for 3D ears matching based on biometrics field with a good steadiness to reduce the false detection rate. The corresponding ear extracts from the side range image and characterized by 3D features. The proposed method used matlab simulation and defined the average detection time 35ms and an identification similarity is 98.25% for the collection of different database. The result shows that the proposed combined method outperforms than the existing of ICP or SCM in terms of detection time and accuracy in training.

AB - Ear recognition is a new technology and future trend for personal identification. However, the false detection rate and matching recognition are very challenging due to the ear complex geometry. The Scope of the study is to introduced a combination of Iterative Closest Point (ICP) and Stochastic Clustering Matching (SCM) algorithm for 3D ears matching based on biometrics field with a good steadiness to reduce the false detection rate. The corresponding ear extracts from the side range image and characterized by 3D features. The proposed method used matlab simulation and defined the average detection time 35ms and an identification similarity is 98.25% for the collection of different database. The result shows that the proposed combined method outperforms than the existing of ICP or SCM in terms of detection time and accuracy in training.

KW - 3D ears matching

KW - Ear identification

KW - Iterative closest point (ICP)

KW - Preprocessing

KW - Stochastic clustering matching (SCM)

UR - http://www.scopus.com/inward/record.url?scp=84894522823&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84894522823&partnerID=8YFLogxK

U2 - 10.3844/jcssp.2014.477.483

DO - 10.3844/jcssp.2014.477.483

M3 - Article

AN - SCOPUS:84894522823

VL - 10

SP - 477

EP - 483

JO - Journal of Computer Science

JF - Journal of Computer Science

SN - 1549-3636

IS - 3

ER -