Adaptive Score Normalization: A Novel Approach for Multimodal Biometric Systems
Multimodal biometric systems integrate the data presented by multiple biometric sources, hence offering a better performance than the systems based on a single biometric modality. Although the coupling of biometric systems can be done at different levels, the fusion at the scores level is the most common since it has been proven effective than the rest of the fusion levels. However, the scores from different modalities are generally heterogeneous. A step of normalizing the scores is needed to transform these scores into a common domain before combining them. In this paper, we study the performance of several normalization techniques with various fusion methods in a context relating to the merger of three unimodal systems based on the face, the palmprint and the fingerprint. We also propose a new adaptive normalization method that takes into account the distribution of client scores and impostor scores. Experiments conducted on a database of 100 people show that the performances of a multimodal system depend on the choice of the normalization method and the fusion technique. The proposed normalization method has given the best results.
[1] A. BenKhalifa, N. Essoukri BenAmara, “Contribution to the Fusion of
Biometric Modalities by the Choquet Integral,” International Journal of
Image, Graphics and Signal Processing, vol. 4, No. 10, 2012, pp. 1 - 7.
[2] A. BenKhalifa, N. Essoukri BenAmara, “Exploration of the Choquet
integral for the fusion of biometric modalities,” IEEE International
Multi-Conference on Systems, Signals & Devices, 2012, pp. 1 - 6.
[3] A. BenKhalifa, N. Essoukri BenAmara, “Bimodal biometric verification
with different fusion levels,” IEEE International Multi-Conference on
Systems, Signals & Devices, 2009, pp. 1 - 6.
[4] A.K Jain, K. Nandakumar, A. Ross, “Score normalization in multimodal
biometric systems,” Pattern Recognition, Vol. 38, No. 12, 2005, pp.
2270 - 2285.
[5] A.K. Jain, A. Ross, S. Prabhakar, “An introduction to biometric
recognition,” IEEE Transactions on Circuits and Systems for Video
Technology, 2004, pp. 4 - 20.
[6] A. K. Jain, A. Ross, “Handbook of Multimodalbiometrics,” Springers,
2007.
[7] C. Wang, Y. Li, H. Zhang, L. Wang, “Classifier Discriminant Analysis
for Face Verification based on FAR-score normalization,” IEEE
Symposium on Foundations of Computational Intelligence, 2007 , pp.
476 - 480.
[8] C. Shu, X. Ding, “Multi-Biometrics Fusion for Identity Verification,”
International Conference on Pattern Recognition, 2006, pp. 493 - 496.
[9] D. Zhang, W.K. Kong, J. You, M. Wong, “On-line palmprint
identification,” IEEE Transactions on Pattern Analysis and Machine
Intelligence, 2003, pp. 1041 - 1050.
[10] D. Hond, L. Spacek “Distinctive Descriptions for Face Processing,”
Proceedings of the 8th British Machine Vision Conference, 1997, pp.
320 - 329.
[11] F. Liu, Q. Zhao, L. Zhang, D. Zhang, “Fingerprint Pore Matching based
on Sparse Representation,” International Conference on Pattern
Recognition, 2010, pp. 1630 - 1633.
[12] F. Perronnin, J.L. Dugelay, “Introduction à la biométrie Authentification
des individus par traitement Audio Vidéo,” Revue traitement du signal,
Vol. 19, No. 4, 2002, pp. 253 - 256.
[13] L. Rzouga, A. BenKhalifa, N. Essoukri BenAmara, “Authentification
multimodale d’un individu par le visage, l’empreinte digitale et la paume
de la main,” Le Colloque de Recherche Appliquée et de Transfert de
Technologie, 2012, pp. 1 - 6.
[14] L. Latha, S. Thangasamy, “Efficient approach to Normalization of
Multimodal Biometric Scores,” International Journal of Computer
Applications, Vol. 32, No. 10, 2011, pp. 57 - 64.
[15] L. Xiuyan, M. Changyun, L. Tiegen, Y. Chenhu, “Research on Personal
Identity Verification Based on Hand Vein Iris and Fingerprint,”
International Symposium on Computer Science and Society, 2011, pp.
16 - 19.
[16] M. Imran, A. Rao, G.H. Kumar, “Multibiometric systems: A
comparative study of multi-algorithmic and multimodal approaches,”
Procedia Computer Science, Vol. 2, 2010, pp. 207 - 212.
[17] R. Snelick, M. Indovina, J. Yen, A. Mink, “Multimodal biometrics:
issues in design and testing,” International Conference on Multimodal
Interfaces, 2003, pp. 68 - 72.
[18] R. Cappelli, D. Maio, D. Maltoni, “Combining fingerprint classifiers,”
International Workshop on Multiple Classifier Systems, 2000, pp. 351 -
361.
[19] R. Giot, C. Rosenberger, “Genetic programming for multibiometrics,”
Expert Systems with Applications, Vol. 39, No. 2, 2012, pp. 1837 -
1847.
[20] P. Ejarque, J. Hernando, “Score bi-Gaussian equalisation for multimodal
person verification,” IET Signal Processing, Vol.3, No. 4, 2009, pp. 322
- 332.
[21] S.J. Horng, Y.H. Chen, R.S. Run, R.J. Chen, J.L Lai, K.O. Sentosal, “An
Improved Score Level Fusion in Multimodal Biometric Systems,”
International Conference on Parallel and Distributed Computing,
Applications and Technologies, 2009, pp. 239 - 246.
[22] S. Ribaric, I. Fratric, “Experimental Evaluation of Matching-Score
Normalization Techniques on Different Multimodal Biometric
Systems,” IEEE Mediterranean Electrotechnical Conference, 2006, pp.
498 - 501.
[23] T. Savič, N. Pavešić, “Personal recognition based on an image of the
palmar surface of the hand,” Pattern Recognition, Vol. 40, No 11, 2007,
pp. 3152 - 3163.
[24] K. Niinuma, P. Unsang, A.K. Jain, “Soft Biometric Traits for
Continuous User Authentication,” IEEE Transactions on Information
Forensics and Security, Vol. 5, No. 4, 2010, pp.771–780.
[1] A. BenKhalifa, N. Essoukri BenAmara, “Contribution to the Fusion of
Biometric Modalities by the Choquet Integral,” International Journal of
Image, Graphics and Signal Processing, vol. 4, No. 10, 2012, pp. 1 - 7.
[2] A. BenKhalifa, N. Essoukri BenAmara, “Exploration of the Choquet
integral for the fusion of biometric modalities,” IEEE International
Multi-Conference on Systems, Signals & Devices, 2012, pp. 1 - 6.
[3] A. BenKhalifa, N. Essoukri BenAmara, “Bimodal biometric verification
with different fusion levels,” IEEE International Multi-Conference on
Systems, Signals & Devices, 2009, pp. 1 - 6.
[4] A.K Jain, K. Nandakumar, A. Ross, “Score normalization in multimodal
biometric systems,” Pattern Recognition, Vol. 38, No. 12, 2005, pp.
2270 - 2285.
[5] A.K. Jain, A. Ross, S. Prabhakar, “An introduction to biometric
recognition,” IEEE Transactions on Circuits and Systems for Video
Technology, 2004, pp. 4 - 20.
[6] A. K. Jain, A. Ross, “Handbook of Multimodalbiometrics,” Springers,
2007.
[7] C. Wang, Y. Li, H. Zhang, L. Wang, “Classifier Discriminant Analysis
for Face Verification based on FAR-score normalization,” IEEE
Symposium on Foundations of Computational Intelligence, 2007 , pp.
476 - 480.
[8] C. Shu, X. Ding, “Multi-Biometrics Fusion for Identity Verification,”
International Conference on Pattern Recognition, 2006, pp. 493 - 496.
[9] D. Zhang, W.K. Kong, J. You, M. Wong, “On-line palmprint
identification,” IEEE Transactions on Pattern Analysis and Machine
Intelligence, 2003, pp. 1041 - 1050.
[10] D. Hond, L. Spacek “Distinctive Descriptions for Face Processing,”
Proceedings of the 8th British Machine Vision Conference, 1997, pp.
320 - 329.
[11] F. Liu, Q. Zhao, L. Zhang, D. Zhang, “Fingerprint Pore Matching based
on Sparse Representation,” International Conference on Pattern
Recognition, 2010, pp. 1630 - 1633.
[12] F. Perronnin, J.L. Dugelay, “Introduction à la biométrie Authentification
des individus par traitement Audio Vidéo,” Revue traitement du signal,
Vol. 19, No. 4, 2002, pp. 253 - 256.
[13] L. Rzouga, A. BenKhalifa, N. Essoukri BenAmara, “Authentification
multimodale d’un individu par le visage, l’empreinte digitale et la paume
de la main,” Le Colloque de Recherche Appliquée et de Transfert de
Technologie, 2012, pp. 1 - 6.
[14] L. Latha, S. Thangasamy, “Efficient approach to Normalization of
Multimodal Biometric Scores,” International Journal of Computer
Applications, Vol. 32, No. 10, 2011, pp. 57 - 64.
[15] L. Xiuyan, M. Changyun, L. Tiegen, Y. Chenhu, “Research on Personal
Identity Verification Based on Hand Vein Iris and Fingerprint,”
International Symposium on Computer Science and Society, 2011, pp.
16 - 19.
[16] M. Imran, A. Rao, G.H. Kumar, “Multibiometric systems: A
comparative study of multi-algorithmic and multimodal approaches,”
Procedia Computer Science, Vol. 2, 2010, pp. 207 - 212.
[17] R. Snelick, M. Indovina, J. Yen, A. Mink, “Multimodal biometrics:
issues in design and testing,” International Conference on Multimodal
Interfaces, 2003, pp. 68 - 72.
[18] R. Cappelli, D. Maio, D. Maltoni, “Combining fingerprint classifiers,”
International Workshop on Multiple Classifier Systems, 2000, pp. 351 -
361.
[19] R. Giot, C. Rosenberger, “Genetic programming for multibiometrics,”
Expert Systems with Applications, Vol. 39, No. 2, 2012, pp. 1837 -
1847.
[20] P. Ejarque, J. Hernando, “Score bi-Gaussian equalisation for multimodal
person verification,” IET Signal Processing, Vol.3, No. 4, 2009, pp. 322
- 332.
[21] S.J. Horng, Y.H. Chen, R.S. Run, R.J. Chen, J.L Lai, K.O. Sentosal, “An
Improved Score Level Fusion in Multimodal Biometric Systems,”
International Conference on Parallel and Distributed Computing,
Applications and Technologies, 2009, pp. 239 - 246.
[22] S. Ribaric, I. Fratric, “Experimental Evaluation of Matching-Score
Normalization Techniques on Different Multimodal Biometric
Systems,” IEEE Mediterranean Electrotechnical Conference, 2006, pp.
498 - 501.
[23] T. Savič, N. Pavešić, “Personal recognition based on an image of the
palmar surface of the hand,” Pattern Recognition, Vol. 40, No 11, 2007,
pp. 3152 - 3163.
[24] K. Niinuma, P. Unsang, A.K. Jain, “Soft Biometric Traits for
Continuous User Authentication,” IEEE Transactions on Information
Forensics and Security, Vol. 5, No. 4, 2010, pp.771–780.
@article{"International Journal of Information, Control and Computer Sciences:65453", author = "Anouar Ben Khalifa and Sami Gazzah and Najoua Essoukri BenAmara", title = "Adaptive Score Normalization: A Novel Approach for Multimodal Biometric Systems", abstract = "Multimodal biometric systems integrate the data presented by multiple biometric sources, hence offering a better performance than the systems based on a single biometric modality. Although the coupling of biometric systems can be done at different levels, the fusion at the scores level is the most common since it has been proven effective than the rest of the fusion levels. However, the scores from different modalities are generally heterogeneous. A step of normalizing the scores is needed to transform these scores into a common domain before combining them. In this paper, we study the performance of several normalization techniques with various fusion methods in a context relating to the merger of three unimodal systems based on the face, the palmprint and the fingerprint. We also propose a new adaptive normalization method that takes into account the distribution of client scores and impostor scores. Experiments conducted on a database of 100 people show that the performances of a multimodal system depend on the choice of the normalization method and the fusion technique. The proposed normalization method has given the best results.
", keywords = "Multibiometrics, Fusion, Score level, Score normalization, Adaptive normalization. ", volume = "7", number = "3", pages = "417-9", }
