Dynamic best spectral bands selection for face recognition

Hamdi Jamel Bouchech, Sebti Foufou, Mongi Abidi

Research output: Contribution to conferencePaper

Abstract

In this paper, face recognition in uncontrolled illumination conditions is investigated. A twofold contribution is proposed. First, three state-of-art algorithms, namely Multiblock Local Binary Pattern (MBLBP), Histogram of Gabor Phase Patterns (HGPP) and Local Gabor Binary Pattern Histogram Sequence (LGBPHS) are evaluated upon the IRIS-M3 face database to study their robustness against a high illumination variation conditions. Second, we propose to use visible multispectral images, provided by the same face database, to enhance the performance of the three mentioned algorithms. To reduce the high data dimensionality introduced by the use of multispectral images, we have designed a system to dynamically select the best spectral bands for each new subject. Our semi-supervised system for best spectral bands selection learn the relation between the recognition performance of each spectral band and its intrinsic quality using techniques of transfer learning and finite mixture of Gaussian for data distribution estimation. The obtained model is function of the image quality, and for each new spectral band, the likelihood ratio test is used to determine if the former belongs to either the set of good spectral bands or bad spectral bands. To the best of our knowledge, this is the first system proposed to dynamically select the best visible spectral bands for face recognition. Our results highlighted further the still challenging problem of face recognition in conditions with high illumination variation, as well as the effectiveness of our subspectral images based approach to increase the accuracy of the studied algorithms by at least 21.66 % upon the proposed database. Finally, our dynamic system has shown a superiority of performance over non-dynamic systems developed for the same face database.

Original languageEnglish (US)
DOIs
StatePublished - Jan 1 2014
Event2014 48th Annual Conference on Information Sciences and Systems, CISS 2014 - Princeton, NJ, United States
Duration: Mar 19 2014Mar 21 2014

Other

Other2014 48th Annual Conference on Information Sciences and Systems, CISS 2014
CountryUnited States
CityPrinceton, NJ
Period3/19/143/21/14

Fingerprint

Face recognition
Lighting
Image quality
Dynamical systems

Keywords

  • bands selection
  • dynamic
  • Face
  • HGPP
  • LGBPHS
  • MBLBP
  • Mixture of Gaussians
  • multispectral
  • Transfer learning
  • uncontrolled illumination

ASJC Scopus subject areas

  • Information Systems

Cite this

Bouchech, H. J., Foufou, S., & Abidi, M. (2014). Dynamic best spectral bands selection for face recognition. Paper presented at 2014 48th Annual Conference on Information Sciences and Systems, CISS 2014, Princeton, NJ, United States. https://doi.org/10.1109/CISS.2014.6814081

Dynamic best spectral bands selection for face recognition. / Bouchech, Hamdi Jamel; Foufou, Sebti; Abidi, Mongi.

2014. Paper presented at 2014 48th Annual Conference on Information Sciences and Systems, CISS 2014, Princeton, NJ, United States.

Research output: Contribution to conferencePaper

Bouchech, HJ, Foufou, S & Abidi, M 2014, 'Dynamic best spectral bands selection for face recognition' Paper presented at 2014 48th Annual Conference on Information Sciences and Systems, CISS 2014, Princeton, NJ, United States, 3/19/14 - 3/21/14, . https://doi.org/10.1109/CISS.2014.6814081
Bouchech HJ, Foufou S, Abidi M. Dynamic best spectral bands selection for face recognition. 2014. Paper presented at 2014 48th Annual Conference on Information Sciences and Systems, CISS 2014, Princeton, NJ, United States. https://doi.org/10.1109/CISS.2014.6814081
Bouchech, Hamdi Jamel ; Foufou, Sebti ; Abidi, Mongi. / Dynamic best spectral bands selection for face recognition. Paper presented at 2014 48th Annual Conference on Information Sciences and Systems, CISS 2014, Princeton, NJ, United States.
@conference{11a66977de4846da9215c10d0a8e7157,
title = "Dynamic best spectral bands selection for face recognition",
abstract = "In this paper, face recognition in uncontrolled illumination conditions is investigated. A twofold contribution is proposed. First, three state-of-art algorithms, namely Multiblock Local Binary Pattern (MBLBP), Histogram of Gabor Phase Patterns (HGPP) and Local Gabor Binary Pattern Histogram Sequence (LGBPHS) are evaluated upon the IRIS-M3 face database to study their robustness against a high illumination variation conditions. Second, we propose to use visible multispectral images, provided by the same face database, to enhance the performance of the three mentioned algorithms. To reduce the high data dimensionality introduced by the use of multispectral images, we have designed a system to dynamically select the best spectral bands for each new subject. Our semi-supervised system for best spectral bands selection learn the relation between the recognition performance of each spectral band and its intrinsic quality using techniques of transfer learning and finite mixture of Gaussian for data distribution estimation. The obtained model is function of the image quality, and for each new spectral band, the likelihood ratio test is used to determine if the former belongs to either the set of good spectral bands or bad spectral bands. To the best of our knowledge, this is the first system proposed to dynamically select the best visible spectral bands for face recognition. Our results highlighted further the still challenging problem of face recognition in conditions with high illumination variation, as well as the effectiveness of our subspectral images based approach to increase the accuracy of the studied algorithms by at least 21.66 {\%} upon the proposed database. Finally, our dynamic system has shown a superiority of performance over non-dynamic systems developed for the same face database.",
keywords = "bands selection, dynamic, Face, HGPP, LGBPHS, MBLBP, Mixture of Gaussians, multispectral, Transfer learning, uncontrolled illumination",
author = "Bouchech, {Hamdi Jamel} and Sebti Foufou and Mongi Abidi",
year = "2014",
month = "1",
day = "1",
doi = "10.1109/CISS.2014.6814081",
language = "English (US)",
note = "2014 48th Annual Conference on Information Sciences and Systems, CISS 2014 ; Conference date: 19-03-2014 Through 21-03-2014",

}

TY - CONF

T1 - Dynamic best spectral bands selection for face recognition

AU - Bouchech, Hamdi Jamel

AU - Foufou, Sebti

AU - Abidi, Mongi

PY - 2014/1/1

Y1 - 2014/1/1

N2 - In this paper, face recognition in uncontrolled illumination conditions is investigated. A twofold contribution is proposed. First, three state-of-art algorithms, namely Multiblock Local Binary Pattern (MBLBP), Histogram of Gabor Phase Patterns (HGPP) and Local Gabor Binary Pattern Histogram Sequence (LGBPHS) are evaluated upon the IRIS-M3 face database to study their robustness against a high illumination variation conditions. Second, we propose to use visible multispectral images, provided by the same face database, to enhance the performance of the three mentioned algorithms. To reduce the high data dimensionality introduced by the use of multispectral images, we have designed a system to dynamically select the best spectral bands for each new subject. Our semi-supervised system for best spectral bands selection learn the relation between the recognition performance of each spectral band and its intrinsic quality using techniques of transfer learning and finite mixture of Gaussian for data distribution estimation. The obtained model is function of the image quality, and for each new spectral band, the likelihood ratio test is used to determine if the former belongs to either the set of good spectral bands or bad spectral bands. To the best of our knowledge, this is the first system proposed to dynamically select the best visible spectral bands for face recognition. Our results highlighted further the still challenging problem of face recognition in conditions with high illumination variation, as well as the effectiveness of our subspectral images based approach to increase the accuracy of the studied algorithms by at least 21.66 % upon the proposed database. Finally, our dynamic system has shown a superiority of performance over non-dynamic systems developed for the same face database.

AB - In this paper, face recognition in uncontrolled illumination conditions is investigated. A twofold contribution is proposed. First, three state-of-art algorithms, namely Multiblock Local Binary Pattern (MBLBP), Histogram of Gabor Phase Patterns (HGPP) and Local Gabor Binary Pattern Histogram Sequence (LGBPHS) are evaluated upon the IRIS-M3 face database to study their robustness against a high illumination variation conditions. Second, we propose to use visible multispectral images, provided by the same face database, to enhance the performance of the three mentioned algorithms. To reduce the high data dimensionality introduced by the use of multispectral images, we have designed a system to dynamically select the best spectral bands for each new subject. Our semi-supervised system for best spectral bands selection learn the relation between the recognition performance of each spectral band and its intrinsic quality using techniques of transfer learning and finite mixture of Gaussian for data distribution estimation. The obtained model is function of the image quality, and for each new spectral band, the likelihood ratio test is used to determine if the former belongs to either the set of good spectral bands or bad spectral bands. To the best of our knowledge, this is the first system proposed to dynamically select the best visible spectral bands for face recognition. Our results highlighted further the still challenging problem of face recognition in conditions with high illumination variation, as well as the effectiveness of our subspectral images based approach to increase the accuracy of the studied algorithms by at least 21.66 % upon the proposed database. Finally, our dynamic system has shown a superiority of performance over non-dynamic systems developed for the same face database.

KW - bands selection

KW - dynamic

KW - Face

KW - HGPP

KW - LGBPHS

KW - MBLBP

KW - Mixture of Gaussians

KW - multispectral

KW - Transfer learning

KW - uncontrolled illumination

UR - http://www.scopus.com/inward/record.url?scp=84901459452&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84901459452&partnerID=8YFLogxK

U2 - 10.1109/CISS.2014.6814081

DO - 10.1109/CISS.2014.6814081

M3 - Paper

AN - SCOPUS:84901459452

ER -