Multimodal Biometric Authentication Using Choquet Integral and Genetic Algorithm
The Choquet integral is a tool for the information fusion that is very effective in the case where fuzzy measures associated with it are well chosen. In this paper, we propose a new approach for calculating fuzzy measures associated with the Choquet integral in a context of data fusion in multimodal biometrics. The proposed approach is based on genetic algorithms. It has been validated in two databases: the first base is relative to synthetic scores and the second one is biometrically relating to the face, fingerprint and palmprint. The results achieved attest the robustness of the proposed approach.
[1] Ben Khalifa, A. Ben Amara, N.E. “Exploration of the Choquet integral
for the fusion of biometric modalities,” IEEE International Multi-
Conference on Systems, Signals and Devices (SSD), 2012, pp. 1 – 6
[2] Ben Khalifa, A. Ben Amara, N.E. “Bimodal biometric verification with
different fusion levels,” IEEE International Multi-Conference on
Systems, Signals and Devices (SSD), 2009, pp. 1 – 6.
[3] Ben Khalifa, A. Ben Amara, N.E. “Fusion at the feature level for person
verification based on off line handwriting and signature,” IEEE
International Conference on Signals, Circuits and Systems, 2008, pp. 1 –
5.
[4] P. Verlinde, P. Druyts, G. Chollet, and M. Acheroy. “A multi-level data
fusion approach for gradually upgrading the performances of identity
verifcation systems,” In Sensor Fusion: Architectures, Algorithms, and
Applications III, volume 3719, Orlando, USA, April 1999.
[5] Ferrer, Miguel A.; Morales, Aythami; Travieso, Carlos M.; Alonso,
Jesws B.; “Low Cost Multimodal Biometric identification System Based
on Hand Geometry, Palm and Finger Print Texture,” IEEE International
Carnahan Conference on Security Technology, 2007. pp.52 – 58.
[6] Israel, S.A.; Scruggs, W.T.; Worek, W.J.; Irvine, J.M.; “Fusing face and
ECG for personal identification,” Proceedings Applied Imagery Pattern
Recognition Workshop, 2003. pp.226 – 231.
[7] Rattani, Ajita; Kisku, D. R.; Bicego, Manuele; Tistarelli, Massimo;
“Robust Feature-Level Multibiometric Classification,” Biometric
Consortium Conference, 2006. pp.1 – 6
[8] Z. Liu and S. Sarkar, “Outdoor recognition at a distance by fusing gait
and face,” Image Vision Comput., vol. 25, pp. 817-832, 2007.
[9] Jinfeng Yang, Xu Zhang, “Feature-level fusion of fingerprint and fingervein
for personal identification,” Pattern Recognition Letters, Volume
33, Issue 5, 1 April 2012, pp. 623-628.
[10] Carlos M. Travieso, Jianguo Zhang, Paul Miller, Jesús B. Alonso,
Miguel A. Ferrer, “Bimodal biometric verification based on face and lips,” Neurocomputing, Volume 74, Issues 14–15, July 2011, pp. 2407-
2410.
[11] Abdallah Meraoumia, Salim Chitroub, Ahmed Bouridane, “Fusion of
Finger-Knuckle-Print and Palmprint for an Efficient Multi-biometric
System of Person Recognition,” IEEE International Conference on
Communications (ICC), 2011, pp. 1 – 5.
[12] S. Pigeon and L. Vandendorpe. “Multiple experts for robust face
authentication,” In Optical Security and Counterfeit Deterrence
Techniques II, pp. 166–177, California, January 1998. Proceedings of
SPIE no 3314.
[13] Jingyan Wang; Yongping Li; Xinyu Ao; Chao Wang; Juan Zhou,
“Multi-modal biometric authentication fusing iris and palmprint based
on GMM,” IEEE Workshop on Statistical Signal Processing, 2009. pp.
349 – 352.
[14] Sheetal Chaudhary, Rajender Nath, “A Multimodal Biometric
Recognition System Based on Fusion of Palmprint, Fingerprint and
Face,” International Conference on Advances in Recent Technologies in
Communication and Computing, 2009, pp. 596-600.
[15] Choquet G., Théorie des capacités. 1953.
[16] Grabisch M., “A new algorithm for identifying fuzzy measures and its
application to pattern recognition,” ICFS 1995, pp. 145-150.
[17] Sugeno M., “Fuzzy measures and fuzzy integrals – A survey,” FADP
1977, pp. 89-102.
[18] Kwak K., Pedrycz W., “Face recognition: A study in information fusion
using fuzzy integral,” Pattern Recognition Letters 26 2005, pp. 719-733.
[19] Andrey Temkoa, Dušan Machob, Climent Nadeua, “Fuzzy integral
based information fusion for classification of highly confusable nonspeech
sounds,” Pattern Recognition 41, 2008, pp. 1814 – 1823.
[20] Hirota, K.; Vu, H.A.; Le, P.Q.; Fatichah, C.; Liu, Z.; Tang, Y.; Tangel,
M.L.; Mu, Z.; Sun, B.; Yan, F.; Masano, D.; Thet, O.; Yamaguchi, M.;
Dong, F.; Yamazaki, Y. “Multimodal gesture recognition based on
Choquet integral,” IEEE International Conference on Fuzzy Systems,
2011, pp. 772 – 776.
[21] Belahcene, M.; Ouamane, A.; Ahmed, A.T. “Fusion by combination of
scores multi-biometric systems,” European Workshop on Visual
Information Processing (EUVIP), 2011, pp. 252 – 257.
[22] Jullien, S.; Valet, L.; Mauris, G.; Bolon, P.; Teyssier, S. “An Attribute
Fusion System Based on the Choquet Integral to Evaluate the Quality of
Composite Parts,” IEEE Transactions on Instrumentation and
Measurement, Volume: 57, Issue: 4, 2008, pp. 755 - 762.
[23] Wen-Chih Lin; Chih-Sheng Huang; Jeng-Ming Yih; Der-Bang Wu;
Yen-Kuei Yu, “Multiple SVM classification syatem based on Choquet
integral with respect to composed measure of L-measure and Deltameasure,”
International Conference on Machine Learning and
Cybernetics (ICMLC), 2010, pp. 2396- 2401.
[24] Kuan-Kai Huang; Jiunn-I Shieh; Kuei-Jen Lee; Shih-Neng Wu,
“Applying a generalized choquet integral with signed fuzzy measure
based on the complexity to evaluate the overall satisfaction of the patients,” International Conference on Machine Learning and
Cybernetics (ICMLC), 2010, pp. 2377- 2382.
[25] Afef Denguir Rekik, Un Cadre Possibiliste pour l’Aide à la Decision
Multicritère et Multi-acteurs Application au Marketing et au
Benchmarking de sites E-commerce, Ph.D. thesis, Université de Savoie
(2007).
[26] Asma Melki, ‘Système d’aide à la régulation et évaluation des transports
multimodaux intégrants les cybercars’, Ph.D. thesis, Ecole Centrale de
Lille (2008).
[27] Mohamed, M.A.; Abou-Elsoud, M.E.; Eid, M.M., “Automated face
recogntion system: Multi-input databases,” International Conference on
Computer Engineering & Systems (ICCES), 2011. pp. 273 – 280.
[28] Rahman, S.; Naim, S.M.; Al Farooq, A.; Islam, M.M., “Curvelet texture
based face recognition using Principal Component Analysis,”
International Conference on Computer and Information Technology
(ICCIT), 2010. pp. 45 - 50.
[29] Neo, H.F.; Teo, C.C.; Teoh, A.B.J., “Development of Partial Face
Recognition Framework,” International Conference on Computer
Graphics, Imaging and Visualization (CGIV), 2010, pp. 142 - 146.
[30] Neo Han Foon; Ying-Han Pang; Jin, A.T.B.; Ling, D.N.C., “An efficient
method for human face recognition using wavelet transform and Zernike
moments,” International Conference on Computer Graphics, Imaging
and Visualization, 2004. pp. 65 - 69.
[31] Nicholl, P.; Bouchaffra, D.; Amira, A.; Perrott, R.H., “Multiresolution
Hybrid Approaches for Automated Face Recognition,” Conference on
Adaptive Hardware and Systems, 2007. pp. 89 - 96.
[32] Jones, C.; Abbott, A.L., “Color face recognition by hypercomplex Gabor
analysis,” International Conference on Automatic Face and Gesture
Recognition, 2006. pp. 1 – 6.
[33] Yinghua Lu; Yao Fu; Jinsong Li; Xiaolu Li; Jun Kong, “A Multi-modal
Authentication Method Based on Human Face and Palmprint,”
International Conference on Future Generation Communication and
Networking, 2008. pp. 193- 196.
[34] Wen-Ying Ma; Sheng Li; Yong-Fang Yao; Chao Lan; Shi-Qiang Gao;
Hui Tang; Xiao-Yuan Jing, “Multi-Modal Biometrics Pixel Level Fusion
and KPCA-RBF Feature Classification for Single Sample Recognition
Problem,” International Congress on Image and Signal Processing,
2009. pp. 1 – 5.
[35] Yong Jian Chin; Thian Song Ong; Goh, M.K.O., Bee Yan Hiew,
“Integrating Palmprint and Fingerprint for Identity Verification,”
International Conference on Network and System Security, 2009. pp.
437- 442.
[36] Kekre, H.B.; Tanuja; Sarode, K.; Tirodkar, A.A, “A study of the efficacy
of using Wavelet Transforms for Palm Print Recognition,” International
Conference on Computing, Communication and Applications (ICCCA),
2012, pp. 1 - 6.
[37] Feng Liu, Qijun Zhao, Lei Zhang, and David Zhang, “Fingerprint Pore
Matching based on Sparse Representation,” International Conference on
Pattern Recognition (ICPR'10), 2010, pp. 1630 – 1633.
[38] Qijun Zhao, Lei Zhang, David Zhang, and Nan Luo, “Direct Pore
Matching for Fingerprint Recognition,” International Conference on
Biometrics, 2009, pp. 597-606.
[39] David Zhang, Wai-Kin Kong, Jane You and Michael Wong, “On-line
palmprint identification,” IEEE Transactions on Pattern Analysis and
Machine Intelligence, pp. 1041-1050, 2003.
[40] Jain, A.K.; Ross, A.; Prabhakar, S, “An introduction to biometric
recognition,” IEEE Transactions on Circuits and Systems for Video
Technology, 2004, pp. 4 - 20.
[41] D. Hond, L. Spacek “Distinctive Descriptions for Face Processing,”
Proceedings of the 8th British Machine Vision Conference BMVC97,
Colchester, England, pp. 320-329,1997.
[42] Holland J.H. “Adaptation in natural and artificial systems,” MIT Press
1975.
[43] Man K.F., Tang K.S., Kwong S. “Genetic algorithms Concepts and
designs,” 2000, Springer.
[1] Ben Khalifa, A. Ben Amara, N.E. “Exploration of the Choquet integral
for the fusion of biometric modalities,” IEEE International Multi-
Conference on Systems, Signals and Devices (SSD), 2012, pp. 1 – 6
[2] Ben Khalifa, A. Ben Amara, N.E. “Bimodal biometric verification with
different fusion levels,” IEEE International Multi-Conference on
Systems, Signals and Devices (SSD), 2009, pp. 1 – 6.
[3] Ben Khalifa, A. Ben Amara, N.E. “Fusion at the feature level for person
verification based on off line handwriting and signature,” IEEE
International Conference on Signals, Circuits and Systems, 2008, pp. 1 –
5.
[4] P. Verlinde, P. Druyts, G. Chollet, and M. Acheroy. “A multi-level data
fusion approach for gradually upgrading the performances of identity
verifcation systems,” In Sensor Fusion: Architectures, Algorithms, and
Applications III, volume 3719, Orlando, USA, April 1999.
[5] Ferrer, Miguel A.; Morales, Aythami; Travieso, Carlos M.; Alonso,
Jesws B.; “Low Cost Multimodal Biometric identification System Based
on Hand Geometry, Palm and Finger Print Texture,” IEEE International
Carnahan Conference on Security Technology, 2007. pp.52 – 58.
[6] Israel, S.A.; Scruggs, W.T.; Worek, W.J.; Irvine, J.M.; “Fusing face and
ECG for personal identification,” Proceedings Applied Imagery Pattern
Recognition Workshop, 2003. pp.226 – 231.
[7] Rattani, Ajita; Kisku, D. R.; Bicego, Manuele; Tistarelli, Massimo;
“Robust Feature-Level Multibiometric Classification,” Biometric
Consortium Conference, 2006. pp.1 – 6
[8] Z. Liu and S. Sarkar, “Outdoor recognition at a distance by fusing gait
and face,” Image Vision Comput., vol. 25, pp. 817-832, 2007.
[9] Jinfeng Yang, Xu Zhang, “Feature-level fusion of fingerprint and fingervein
for personal identification,” Pattern Recognition Letters, Volume
33, Issue 5, 1 April 2012, pp. 623-628.
[10] Carlos M. Travieso, Jianguo Zhang, Paul Miller, Jesús B. Alonso,
Miguel A. Ferrer, “Bimodal biometric verification based on face and lips,” Neurocomputing, Volume 74, Issues 14–15, July 2011, pp. 2407-
2410.
[11] Abdallah Meraoumia, Salim Chitroub, Ahmed Bouridane, “Fusion of
Finger-Knuckle-Print and Palmprint for an Efficient Multi-biometric
System of Person Recognition,” IEEE International Conference on
Communications (ICC), 2011, pp. 1 – 5.
[12] S. Pigeon and L. Vandendorpe. “Multiple experts for robust face
authentication,” In Optical Security and Counterfeit Deterrence
Techniques II, pp. 166–177, California, January 1998. Proceedings of
SPIE no 3314.
[13] Jingyan Wang; Yongping Li; Xinyu Ao; Chao Wang; Juan Zhou,
“Multi-modal biometric authentication fusing iris and palmprint based
on GMM,” IEEE Workshop on Statistical Signal Processing, 2009. pp.
349 – 352.
[14] Sheetal Chaudhary, Rajender Nath, “A Multimodal Biometric
Recognition System Based on Fusion of Palmprint, Fingerprint and
Face,” International Conference on Advances in Recent Technologies in
Communication and Computing, 2009, pp. 596-600.
[15] Choquet G., Théorie des capacités. 1953.
[16] Grabisch M., “A new algorithm for identifying fuzzy measures and its
application to pattern recognition,” ICFS 1995, pp. 145-150.
[17] Sugeno M., “Fuzzy measures and fuzzy integrals – A survey,” FADP
1977, pp. 89-102.
[18] Kwak K., Pedrycz W., “Face recognition: A study in information fusion
using fuzzy integral,” Pattern Recognition Letters 26 2005, pp. 719-733.
[19] Andrey Temkoa, Dušan Machob, Climent Nadeua, “Fuzzy integral
based information fusion for classification of highly confusable nonspeech
sounds,” Pattern Recognition 41, 2008, pp. 1814 – 1823.
[20] Hirota, K.; Vu, H.A.; Le, P.Q.; Fatichah, C.; Liu, Z.; Tang, Y.; Tangel,
M.L.; Mu, Z.; Sun, B.; Yan, F.; Masano, D.; Thet, O.; Yamaguchi, M.;
Dong, F.; Yamazaki, Y. “Multimodal gesture recognition based on
Choquet integral,” IEEE International Conference on Fuzzy Systems,
2011, pp. 772 – 776.
[21] Belahcene, M.; Ouamane, A.; Ahmed, A.T. “Fusion by combination of
scores multi-biometric systems,” European Workshop on Visual
Information Processing (EUVIP), 2011, pp. 252 – 257.
[22] Jullien, S.; Valet, L.; Mauris, G.; Bolon, P.; Teyssier, S. “An Attribute
Fusion System Based on the Choquet Integral to Evaluate the Quality of
Composite Parts,” IEEE Transactions on Instrumentation and
Measurement, Volume: 57, Issue: 4, 2008, pp. 755 - 762.
[23] Wen-Chih Lin; Chih-Sheng Huang; Jeng-Ming Yih; Der-Bang Wu;
Yen-Kuei Yu, “Multiple SVM classification syatem based on Choquet
integral with respect to composed measure of L-measure and Deltameasure,”
International Conference on Machine Learning and
Cybernetics (ICMLC), 2010, pp. 2396- 2401.
[24] Kuan-Kai Huang; Jiunn-I Shieh; Kuei-Jen Lee; Shih-Neng Wu,
“Applying a generalized choquet integral with signed fuzzy measure
based on the complexity to evaluate the overall satisfaction of the patients,” International Conference on Machine Learning and
Cybernetics (ICMLC), 2010, pp. 2377- 2382.
[25] Afef Denguir Rekik, Un Cadre Possibiliste pour l’Aide à la Decision
Multicritère et Multi-acteurs Application au Marketing et au
Benchmarking de sites E-commerce, Ph.D. thesis, Université de Savoie
(2007).
[26] Asma Melki, ‘Système d’aide à la régulation et évaluation des transports
multimodaux intégrants les cybercars’, Ph.D. thesis, Ecole Centrale de
Lille (2008).
[27] Mohamed, M.A.; Abou-Elsoud, M.E.; Eid, M.M., “Automated face
recogntion system: Multi-input databases,” International Conference on
Computer Engineering & Systems (ICCES), 2011. pp. 273 – 280.
[28] Rahman, S.; Naim, S.M.; Al Farooq, A.; Islam, M.M., “Curvelet texture
based face recognition using Principal Component Analysis,”
International Conference on Computer and Information Technology
(ICCIT), 2010. pp. 45 - 50.
[29] Neo, H.F.; Teo, C.C.; Teoh, A.B.J., “Development of Partial Face
Recognition Framework,” International Conference on Computer
Graphics, Imaging and Visualization (CGIV), 2010, pp. 142 - 146.
[30] Neo Han Foon; Ying-Han Pang; Jin, A.T.B.; Ling, D.N.C., “An efficient
method for human face recognition using wavelet transform and Zernike
moments,” International Conference on Computer Graphics, Imaging
and Visualization, 2004. pp. 65 - 69.
[31] Nicholl, P.; Bouchaffra, D.; Amira, A.; Perrott, R.H., “Multiresolution
Hybrid Approaches for Automated Face Recognition,” Conference on
Adaptive Hardware and Systems, 2007. pp. 89 - 96.
[32] Jones, C.; Abbott, A.L., “Color face recognition by hypercomplex Gabor
analysis,” International Conference on Automatic Face and Gesture
Recognition, 2006. pp. 1 – 6.
[33] Yinghua Lu; Yao Fu; Jinsong Li; Xiaolu Li; Jun Kong, “A Multi-modal
Authentication Method Based on Human Face and Palmprint,”
International Conference on Future Generation Communication and
Networking, 2008. pp. 193- 196.
[34] Wen-Ying Ma; Sheng Li; Yong-Fang Yao; Chao Lan; Shi-Qiang Gao;
Hui Tang; Xiao-Yuan Jing, “Multi-Modal Biometrics Pixel Level Fusion
and KPCA-RBF Feature Classification for Single Sample Recognition
Problem,” International Congress on Image and Signal Processing,
2009. pp. 1 – 5.
[35] Yong Jian Chin; Thian Song Ong; Goh, M.K.O., Bee Yan Hiew,
“Integrating Palmprint and Fingerprint for Identity Verification,”
International Conference on Network and System Security, 2009. pp.
437- 442.
[36] Kekre, H.B.; Tanuja; Sarode, K.; Tirodkar, A.A, “A study of the efficacy
of using Wavelet Transforms for Palm Print Recognition,” International
Conference on Computing, Communication and Applications (ICCCA),
2012, pp. 1 - 6.
[37] Feng Liu, Qijun Zhao, Lei Zhang, and David Zhang, “Fingerprint Pore
Matching based on Sparse Representation,” International Conference on
Pattern Recognition (ICPR'10), 2010, pp. 1630 – 1633.
[38] Qijun Zhao, Lei Zhang, David Zhang, and Nan Luo, “Direct Pore
Matching for Fingerprint Recognition,” International Conference on
Biometrics, 2009, pp. 597-606.
[39] David Zhang, Wai-Kin Kong, Jane You and Michael Wong, “On-line
palmprint identification,” IEEE Transactions on Pattern Analysis and
Machine Intelligence, pp. 1041-1050, 2003.
[40] Jain, A.K.; Ross, A.; Prabhakar, S, “An introduction to biometric
recognition,” IEEE Transactions on Circuits and Systems for Video
Technology, 2004, pp. 4 - 20.
[41] D. Hond, L. Spacek “Distinctive Descriptions for Face Processing,”
Proceedings of the 8th British Machine Vision Conference BMVC97,
Colchester, England, pp. 320-329,1997.
[42] Holland J.H. “Adaptation in natural and artificial systems,” MIT Press
1975.
[43] Man K.F., Tang K.S., Kwong S. “Genetic algorithms Concepts and
designs,” 2000, Springer.
@article{"International Journal of Information, Control and Computer Sciences:65454", author = "Anouar Ben Khalifa and Sami Gazzah and Najoua Essoukri BenAmara", title = "Multimodal Biometric Authentication Using Choquet Integral and Genetic Algorithm ", abstract = "The Choquet integral is a tool for the information fusion that is very effective in the case where fuzzy measures associated with it are well chosen. In this paper, we propose a new approach for calculating fuzzy measures associated with the Choquet integral in a context of data fusion in multimodal biometrics. The proposed approach is based on genetic algorithms. It has been validated in two databases: the first base is relative to synthetic scores and the second one is biometrically relating to the face, fingerprint and palmprint. The results achieved attest the robustness of the proposed approach.
", keywords = "Multimodal biometrics, data fusion, Choquet integral, fuzzy measures, genetic algorithm. ", volume = "7", number = "3", pages = "426-10", }
