Face recognition using local geometrical features - PCA with Euclidean classifier

Fatimah Khalid, Tengku Mohd Tengku Sembok, Khairuddin Omar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

The goal of this research is to get the minimum features and produce better recognition rates. Before doing the feature selection, we investigate automatic methods for detecting face anchor points with 412 3D-facial points of 60 individuals. There are 7 images per subject including views presenting light rotations and facial expressions. Each images have twelve anchor points which are Right Outer Eye, Right Inner Eye, Left Outer Eye, Left Inner Eye, Upper nose point, Nose Tip,Right Nose Base, Left Nose Base, Right Outer Face, Left Outer Face, Chin, and Upper Face. All the control points are based on the measurement on an absolute scale (mm). After all the control points have been determined, we will extract a relevant set of features. These features are classified in 3 : (1) distance of mass points, (2) angle measurements, and (3) angle measurements. There are fifty-three local geometrical features extracted from 3D points human faces to model the face for face recognition and the discriminating power calculation is to show the valuable feature among all the features. Experiment performed on the GavabDB dataset (412 faces) show that our algorithm achieved 86% of success when respectively the first rank matched.

Original languageEnglish
Title of host publicationProceedings - International Symposium on Information Technology 2008, ITSim
Volume2
DOIs
Publication statusPublished - 2008
EventInternational Symposium on Information Technology 2008, ITSim - Kuala Lumpur
Duration: 26 Aug 200829 Aug 2008

Other

OtherInternational Symposium on Information Technology 2008, ITSim
CityKuala Lumpur
Period26/8/0829/8/08

Fingerprint

Angle measurement
Face recognition
Anchors
Classifiers
Feature extraction
Experiments

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Khalid, F., Sembok, T. M. T., & Omar, K. (2008). Face recognition using local geometrical features - PCA with Euclidean classifier. In Proceedings - International Symposium on Information Technology 2008, ITSim (Vol. 2). [4631687] https://doi.org/10.1109/ITSIM.2008.4631687

Face recognition using local geometrical features - PCA with Euclidean classifier. / Khalid, Fatimah; Sembok, Tengku Mohd Tengku; Omar, Khairuddin.

Proceedings - International Symposium on Information Technology 2008, ITSim. Vol. 2 2008. 4631687.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Khalid, F, Sembok, TMT & Omar, K 2008, Face recognition using local geometrical features - PCA with Euclidean classifier. in Proceedings - International Symposium on Information Technology 2008, ITSim. vol. 2, 4631687, International Symposium on Information Technology 2008, ITSim, Kuala Lumpur, 26/8/08. https://doi.org/10.1109/ITSIM.2008.4631687
Khalid F, Sembok TMT, Omar K. Face recognition using local geometrical features - PCA with Euclidean classifier. In Proceedings - International Symposium on Information Technology 2008, ITSim. Vol. 2. 2008. 4631687 https://doi.org/10.1109/ITSIM.2008.4631687
Khalid, Fatimah ; Sembok, Tengku Mohd Tengku ; Omar, Khairuddin. / Face recognition using local geometrical features - PCA with Euclidean classifier. Proceedings - International Symposium on Information Technology 2008, ITSim. Vol. 2 2008.
@inproceedings{85251d864f714c1baf5e67a4a567e2d9,
title = "Face recognition using local geometrical features - PCA with Euclidean classifier",
abstract = "The goal of this research is to get the minimum features and produce better recognition rates. Before doing the feature selection, we investigate automatic methods for detecting face anchor points with 412 3D-facial points of 60 individuals. There are 7 images per subject including views presenting light rotations and facial expressions. Each images have twelve anchor points which are Right Outer Eye, Right Inner Eye, Left Outer Eye, Left Inner Eye, Upper nose point, Nose Tip,Right Nose Base, Left Nose Base, Right Outer Face, Left Outer Face, Chin, and Upper Face. All the control points are based on the measurement on an absolute scale (mm). After all the control points have been determined, we will extract a relevant set of features. These features are classified in 3 : (1) distance of mass points, (2) angle measurements, and (3) angle measurements. There are fifty-three local geometrical features extracted from 3D points human faces to model the face for face recognition and the discriminating power calculation is to show the valuable feature among all the features. Experiment performed on the GavabDB dataset (412 faces) show that our algorithm achieved 86{\%} of success when respectively the first rank matched.",
author = "Fatimah Khalid and Sembok, {Tengku Mohd Tengku} and Khairuddin Omar",
year = "2008",
doi = "10.1109/ITSIM.2008.4631687",
language = "English",
isbn = "9781424423286",
volume = "2",
booktitle = "Proceedings - International Symposium on Information Technology 2008, ITSim",

}

TY - GEN

T1 - Face recognition using local geometrical features - PCA with Euclidean classifier

AU - Khalid, Fatimah

AU - Sembok, Tengku Mohd Tengku

AU - Omar, Khairuddin

PY - 2008

Y1 - 2008

N2 - The goal of this research is to get the minimum features and produce better recognition rates. Before doing the feature selection, we investigate automatic methods for detecting face anchor points with 412 3D-facial points of 60 individuals. There are 7 images per subject including views presenting light rotations and facial expressions. Each images have twelve anchor points which are Right Outer Eye, Right Inner Eye, Left Outer Eye, Left Inner Eye, Upper nose point, Nose Tip,Right Nose Base, Left Nose Base, Right Outer Face, Left Outer Face, Chin, and Upper Face. All the control points are based on the measurement on an absolute scale (mm). After all the control points have been determined, we will extract a relevant set of features. These features are classified in 3 : (1) distance of mass points, (2) angle measurements, and (3) angle measurements. There are fifty-three local geometrical features extracted from 3D points human faces to model the face for face recognition and the discriminating power calculation is to show the valuable feature among all the features. Experiment performed on the GavabDB dataset (412 faces) show that our algorithm achieved 86% of success when respectively the first rank matched.

AB - The goal of this research is to get the minimum features and produce better recognition rates. Before doing the feature selection, we investigate automatic methods for detecting face anchor points with 412 3D-facial points of 60 individuals. There are 7 images per subject including views presenting light rotations and facial expressions. Each images have twelve anchor points which are Right Outer Eye, Right Inner Eye, Left Outer Eye, Left Inner Eye, Upper nose point, Nose Tip,Right Nose Base, Left Nose Base, Right Outer Face, Left Outer Face, Chin, and Upper Face. All the control points are based on the measurement on an absolute scale (mm). After all the control points have been determined, we will extract a relevant set of features. These features are classified in 3 : (1) distance of mass points, (2) angle measurements, and (3) angle measurements. There are fifty-three local geometrical features extracted from 3D points human faces to model the face for face recognition and the discriminating power calculation is to show the valuable feature among all the features. Experiment performed on the GavabDB dataset (412 faces) show that our algorithm achieved 86% of success when respectively the first rank matched.

UR - http://www.scopus.com/inward/record.url?scp=57349183138&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=57349183138&partnerID=8YFLogxK

U2 - 10.1109/ITSIM.2008.4631687

DO - 10.1109/ITSIM.2008.4631687

M3 - Conference contribution

SN - 9781424423286

VL - 2

BT - Proceedings - International Symposium on Information Technology 2008, ITSim

ER -