Performance Evaluation of Face Recognition Algorithms on the Asian Face Database, KFDB

Bon Woo Hwang, Hyeran Byun, Myoung Cheol Roh, Seong Whan Lee

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

Human face is one of the most common and useful keys to a person's identity. Many algorithms have been developed for automatic face recognition. And a number of commercial products have reached the market already. In general, however, many believe that the technology has yet to improve further, particularly to overcome the instability due to variable illuminations, expressions, poses and accessories. These variations often lead to large nonlinear variation in facial image. To date it is a very important issue to understand the limitation of the current face recognition technology. In this paper, we report the experimental result of face recognition performed using PCA (Principal Component Analysis), LFA (Local Feature Analysis) and correlation matching algorithms on the KFDB (Korean Face Database) which contains Korean face images taken under various conditions.

Original languageEnglish
Pages (from-to)557-565
Number of pages9
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2688
Publication statusPublished - 2003 Dec 1

Fingerprint

Recognition Algorithm
Face recognition
Face Recognition
Performance Evaluation
Face
Databases
Technology
Local Features
Accessories
Matching Algorithm
Principal Component Analysis
Lighting
Principal component analysis
Illumination
Person
Experimental Results
Facial Recognition

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

@article{899f045cee704e8bbee8180fc56ec06a,
title = "Performance Evaluation of Face Recognition Algorithms on the Asian Face Database, KFDB",
abstract = "Human face is one of the most common and useful keys to a person's identity. Many algorithms have been developed for automatic face recognition. And a number of commercial products have reached the market already. In general, however, many believe that the technology has yet to improve further, particularly to overcome the instability due to variable illuminations, expressions, poses and accessories. These variations often lead to large nonlinear variation in facial image. To date it is a very important issue to understand the limitation of the current face recognition technology. In this paper, we report the experimental result of face recognition performed using PCA (Principal Component Analysis), LFA (Local Feature Analysis) and correlation matching algorithms on the KFDB (Korean Face Database) which contains Korean face images taken under various conditions.",
author = "Hwang, {Bon Woo} and Hyeran Byun and Roh, {Myoung Cheol} and Lee, {Seong Whan}",
year = "2003",
month = "12",
day = "1",
language = "English",
volume = "2688",
pages = "557--565",
journal = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
issn = "0302-9743",
publisher = "Springer Verlag",

}

TY - JOUR

T1 - Performance Evaluation of Face Recognition Algorithms on the Asian Face Database, KFDB

AU - Hwang, Bon Woo

AU - Byun, Hyeran

AU - Roh, Myoung Cheol

AU - Lee, Seong Whan

PY - 2003/12/1

Y1 - 2003/12/1

N2 - Human face is one of the most common and useful keys to a person's identity. Many algorithms have been developed for automatic face recognition. And a number of commercial products have reached the market already. In general, however, many believe that the technology has yet to improve further, particularly to overcome the instability due to variable illuminations, expressions, poses and accessories. These variations often lead to large nonlinear variation in facial image. To date it is a very important issue to understand the limitation of the current face recognition technology. In this paper, we report the experimental result of face recognition performed using PCA (Principal Component Analysis), LFA (Local Feature Analysis) and correlation matching algorithms on the KFDB (Korean Face Database) which contains Korean face images taken under various conditions.

AB - Human face is one of the most common and useful keys to a person's identity. Many algorithms have been developed for automatic face recognition. And a number of commercial products have reached the market already. In general, however, many believe that the technology has yet to improve further, particularly to overcome the instability due to variable illuminations, expressions, poses and accessories. These variations often lead to large nonlinear variation in facial image. To date it is a very important issue to understand the limitation of the current face recognition technology. In this paper, we report the experimental result of face recognition performed using PCA (Principal Component Analysis), LFA (Local Feature Analysis) and correlation matching algorithms on the KFDB (Korean Face Database) which contains Korean face images taken under various conditions.

UR - http://www.scopus.com/inward/record.url?scp=34548742699&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34548742699&partnerID=8YFLogxK

M3 - Article

VL - 2688

SP - 557

EP - 565

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SN - 0302-9743

ER -