Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis

Soltane Mohamed

doi:doi:10.11648/j.mlr.20170201.15

| Peer-Reviewed

Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis

Soltane Mohamed

Published in Machine Learning Research (Volume 2, Issue 1)

Received: 12 January 2017 Accepted: 16 February 2017 Published: 3 March 2017

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Face recognition has long been a goal of computer vision, but only in recent years reliable automated face recognition has become a realistic target of biometrics research. In this paper the contribution of classifier analysis to the Face Biometrics Verification performance is examined. It refers to the paradigm that in classification tasks, the use of multiple observations and their judicious fusion at the data, hence the decision fusions at different levels improve the correct decision performance. The fusion tasks reported in this work were carried through fusion of two well-known face recognizers, ICA I and ICA II. It incorporates the decision at matching score level, a novel fusion strategy is employed; the Likelihood Ratio Fusion within scores. This strategy increases the accuracy of the face recognition system and at the same time reduces the limitations of individual recognizer. The performance of the analysis studies were tested based on three different face databases ORL 94, Indian face database and eNTERFACE2005 Dynamic Face Database and the simulation results are showed a significant performance achievements.

Published in	Machine Learning Research (Volume 2, Issue 1)
DOI	10.11648/j.mlr.20170201.15
Page(s)	35-50
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Classifier, Fusion, Biometrics, Face Verification, PCA, LDA, ICA, Likelihood Parameter Estimate

References

[1]	S. Gleni and P. Petratos, DNA Smart Card for Financial Transactions, The ACM Student Magazine 2004, http://www.acm.org.
[2]	G. Chetty and M. Wagner, Audio-Visual Multimodal Fusion for Biometric Person Authentication and Liveness Verification, Australian Computer Society, Inc. This paper appeared at the NICTA-HCSNet Multimodal UserInteraction Workshop (MMUI2005), Sydney, Australia.
[3]	N. Poh and S. Bengio, Database, Protocol and Tools for Evaluating Score-Level Fusion Algorithms in Biometric Authentication, IDIAP RR 04-44, August 2004, a IDIAP, CP 592, 1920 Martigny, Switzerland.
[4]	S. Srisuk, M. Petrou, W. Kurutach and A. Kadyrov, Face Authentication using the Trace Transform, Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’03).
[5]	S. Ben-Yacoub, Y. Abdeljaoued, and E. Mayoraz, Fusion of Face and Speech Data for Person Identity Verification, IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 10, NO. 5, SEPTEMBER 1999.
[6]	J. Kittler, M. Hatef, Robert P. W. Duin, and J. Matas, On Combining Classifiers, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 3, MARCH 1998.
[7]	B. Batagelj and F. Solina, Face Recognition in Different Subspaces: A Comparative Study, University of Ljubljana, Faculty of Computer and Information Science, Trzaska 25, SI-1000 Ljubljana, Slovenia.
[8]	X. Lu, Y. Wang and A.K. Jain, Combining Classifiers for Face Recognition, Proc. ICME 2003 (IEEE International Conference on Multimedia & Expo), Baltimore, MD, July 6-9, 2003, pp. 13-16.
[9]	Brian C. Becker and Enrique G. Ortiz , Evaluation of Face Recognition Techniques for Application to Face-book, 8th IEEE International Conference on Automatic Face & Gesture Recognition, 2008 (FG '08)., December, 2009.
[10]	K. Delac, M. Grgic, S. Grgic, Independent Comparative study of PCA, ICA and LDA on the FERET Data set, Proc. of the 4th International Symposium on Image and Signal Processing and Analysis, pp 289-294, 2005.
[11]	K. Veeramachaneni, L. Osadciw, A. Ross and N. Srinivas, Decision-level Fusion Strategies for Correlated Biometric Classifiers, Proc. of IEEE Computer Society Workshop on Biometrics at the Computer Vision and Pattern Recogniton (CVPR) conference, (Anchorage, USA), June 2008.
[12]	H. K. Ekenel and B. Sankur, Multiresolution face recognition, Electrical and Electronic Engineering Department, Bogazici University, Bebek, I˙stanbul, Turkey-Received 30 January 2004; accepted 17 September 2004. Elsevier B. V.
[13]	Olivetti Research Laboratories face database, ORL 1994, http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.
[14]	F. Samaria and A. Harter, Parameterization of a stochastic model for human face identification, 2nd IEEE Workshop on Applications of Computer Vision, Sarasota (Florida), December 1994.
[15]	Vidit Jain and Amitabha Mukherjee. The Indian Face Database 2002. http://vis-www.cs.umass.edu/~vidit/IndianFaceDatabase/.
[16]	Y. Zana, Roberto M. Cesar-Jr, Rogerio S. Feris, and Matthew Turk, Face Verification in Polar Frequency Domain: A Biologically Motivated Approach, ISVC 2005, LNCS 3804, pp. 183–190, 2005. C Springer-Verlag Berlin Heidelberg 2005
[17]	M. Turk and A. Pentland, Eigenfaces for Recognition, Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
[18]	G. Shakhnarovich and B. Moghaddam, Face Recognition in Subspaces, TR2004-041 May 2004. Electric Research Laboratories, Inc., 2004 - 201 Broadway, Cambridge, Massachusetts 02139.
[19]	M. M. Loève. Probability Theory, Van Nostrand, Princeton, 1955.
[20]	V. Belhumeur, J. Hespanha, and D. Kriegman, Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19 (7): 711-720, July 1997.
[21]	S. Bn-Yacoub, Y. Abdljaoued and E. Mayoraz, FUSION OF FACE AND SPEECH DATA FOR PERSON IDENTITY VERIFICATION, IDIAP-RR 99-2003.
[22]	A. Hyvärinen and E. Oja, Independent Component Analysis: Algorithms and Applications, Neural Networks Research Centre Helsinki University of Technology, P. O. Box 5400, FIN-02015 HUT, Finland Neural Networks, 13 (4-5): 411-430, 2000.
[23]	W. Lu and Jagath C. Rajapakse, Constrained Independent Component Analysis, School of Computer Engineering – Nanyang Technological University, Singapore 639798.
[24]	S. Vaseghi and H. Jetelova, Principal and independent component analysis in image processing, in Proceedings of the 14th ACM International Conference on Mobile Computing and Networking (MOBICOM ’06) , pp. 1–5, San Francisco, Calif, USA, September 2006.
[25]	M. S. Bartlett, H. M. Lades, T. J. Sejnowski, Independent component representations for face recognition, Conference on Human Vision and Electronic Imaging III, San Jose, California, 1998.
[26]	P. Comon, Independent component analysis, a new concept, Signal Processing 36, 287–314 (1994).
[27]	A. Hyvärinen, Independent component analysis by minimization of mutual information, Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP’97), 3917–3920 (1997).
[28]	K. Veeramachaneni, L. A. Osadciw, and P. K. Varshney, An Adaptive Multimodal Biometric Management Algorithm, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS-PART C: APPLICATIONS AND REVIEWS, VOL. 35, NO. 3, AUGUST 2005.
[29]	D. Huang, H. Leung and Winston Li, Fusion of Dependent and Independent Biometric Information Sources, Department of Electrical & Computer Engineering - University of Calgary, 2500 University Dr NW Calgary, AB T2N 1N4.
[30]	P. Minh Tri, On estimating the parameters of Gaussian mixtures using EM, School of Computer Engineering, Nanyang Technological University.
[31]	Distance Measures Overview, https://docs.tibco.com/pub/spotfire/5.5.0-march-2013/UsersGuide/hc/hc_distance_measures_overview.htm
[32]	Distance Measures, http://www.umass.edu/landeco/teaching/multivariate/readings/McCune.and.Grace. 2002.chapter6.pdf.
[33]	Yannis S., Yannis P., Felipe C., Pedro L., Francois S., Sascha S., Rolando B., Federico M., and Athanasios V., GMM-Based Multimodal Biometric Verification, eNTERFACE 2005 The summer Workshop on Multimodal Interfaces July 18^th – August 12^th, Facultè Polytechnique de Mons, Belgium.

Cite This Article

Plain Text BibTeX RIS

APA Style

Soltane Mohamed. (2017). Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis. Machine Learning Research, 2(1), 35-50. https://doi.org/10.11648/j.mlr.20170201.15

Copy | Download

ACS Style

Soltane Mohamed. Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis. Mach. Learn. Res. 2017, 2(1), 35-50. doi: 10.11648/j.mlr.20170201.15

Copy | Download

AMA Style

Soltane Mohamed. Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis. Mach Learn Res. 2017;2(1):35-50. doi: 10.11648/j.mlr.20170201.15

Copy | Download

@article{10.11648/j.mlr.20170201.15,
  author = {Soltane Mohamed},
  title = {Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis},
  journal = {Machine Learning Research},
  volume = {2},
  number = {1},
  pages = {35-50},
  doi = {10.11648/j.mlr.20170201.15},
  url = {https://doi.org/10.11648/j.mlr.20170201.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20170201.15},
  abstract = {Face recognition has long been a goal of computer vision, but only in recent years reliable automated face recognition has become a realistic target of biometrics research. In this paper the contribution of classifier analysis to the Face Biometrics Verification performance is examined. It refers to the paradigm that in classification tasks, the use of multiple observations and their judicious fusion at the data, hence the decision fusions at different levels improve the correct decision performance. The fusion tasks reported in this work were carried through fusion of two well-known face recognizers, ICA I and ICA II. It incorporates the decision at matching score level, a novel fusion strategy is employed; the Likelihood Ratio Fusion within scores. This strategy increases the accuracy of the face recognition system and at the same time reduces the limitations of individual recognizer. The performance of the analysis studies were tested based on three different face databases ORL 94, Indian face database and eNTERFACE2005 Dynamic Face Database and the simulation results are showed a significant performance achievements.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis
AU  - Soltane Mohamed
Y1  - 2017/03/03
PY  - 2017
N1  - https://doi.org/10.11648/j.mlr.20170201.15
DO  - 10.11648/j.mlr.20170201.15
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 35
EP  - 50
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20170201.15
AB  - Face recognition has long been a goal of computer vision, but only in recent years reliable automated face recognition has become a realistic target of biometrics research. In this paper the contribution of classifier analysis to the Face Biometrics Verification performance is examined. It refers to the paradigm that in classification tasks, the use of multiple observations and their judicious fusion at the data, hence the decision fusions at different levels improve the correct decision performance. The fusion tasks reported in this work were carried through fusion of two well-known face recognizers, ICA I and ICA II. It incorporates the decision at matching score level, a novel fusion strategy is employed; the Likelihood Ratio Fusion within scores. This strategy increases the accuracy of the face recognition system and at the same time reduces the limitations of individual recognizer. The performance of the analysis studies were tested based on three different face databases ORL 94, Indian face database and eNTERFACE2005 Dynamic Face Database and the simulation results are showed a significant performance achievements.
VL  - 2
IS  - 1
ER  -

Copy | Download

Author Information

Soltane Mohamed

Electrical Engineering & Computing Department, Faculty of Sciences & Technology, Doctor Yahia Fares University of Medea, Medea, Algeria

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Soltane Mohamed. (2017). Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis. Machine Learning Research, 2(1), 35-50. https://doi.org/10.11648/j.mlr.20170201.15

Copy | Download

ACS Style

Soltane Mohamed. Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis. Mach. Learn. Res. 2017, 2(1), 35-50. doi: 10.11648/j.mlr.20170201.15

Copy | Download

AMA Style

Soltane Mohamed. Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis. Mach Learn Res. 2017;2(1):35-50. doi: 10.11648/j.mlr.20170201.15

Copy | Download

@article{10.11648/j.mlr.20170201.15,
  author = {Soltane Mohamed},
  title = {Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis},
  journal = {Machine Learning Research},
  volume = {2},
  number = {1},
  pages = {35-50},
  doi = {10.11648/j.mlr.20170201.15},
  url = {https://doi.org/10.11648/j.mlr.20170201.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20170201.15},
  abstract = {Face recognition has long been a goal of computer vision, but only in recent years reliable automated face recognition has become a realistic target of biometrics research. In this paper the contribution of classifier analysis to the Face Biometrics Verification performance is examined. It refers to the paradigm that in classification tasks, the use of multiple observations and their judicious fusion at the data, hence the decision fusions at different levels improve the correct decision performance. The fusion tasks reported in this work were carried through fusion of two well-known face recognizers, ICA I and ICA II. It incorporates the decision at matching score level, a novel fusion strategy is employed; the Likelihood Ratio Fusion within scores. This strategy increases the accuracy of the face recognition system and at the same time reduces the limitations of individual recognizer. The performance of the analysis studies were tested based on three different face databases ORL 94, Indian face database and eNTERFACE2005 Dynamic Face Database and the simulation results are showed a significant performance achievements.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis
AU  - Soltane Mohamed
Y1  - 2017/03/03
PY  - 2017
N1  - https://doi.org/10.11648/j.mlr.20170201.15
DO  - 10.11648/j.mlr.20170201.15
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 35
EP  - 50
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20170201.15
AB  - Face recognition has long been a goal of computer vision, but only in recent years reliable automated face recognition has become a realistic target of biometrics research. In this paper the contribution of classifier analysis to the Face Biometrics Verification performance is examined. It refers to the paradigm that in classification tasks, the use of multiple observations and their judicious fusion at the data, hence the decision fusions at different levels improve the correct decision performance. The fusion tasks reported in this work were carried through fusion of two well-known face recognizers, ICA I and ICA II. It incorporates the decision at matching score level, a novel fusion strategy is employed; the Likelihood Ratio Fusion within scores. This strategy increases the accuracy of the face recognition system and at the same time reduces the limitations of individual recognizer. The performance of the analysis studies were tested based on three different face databases ORL 94, Indian face database and eNTERFACE2005 Dynamic Face Database and the simulation results are showed a significant performance achievements.
VL  - 2
IS  - 1
ER  -

Copy | Download