A Dynamic Sequential Approach Using Deep Learning to Improve the Performance of Biometrics Match on Card Systems
Subject Areas : electrical and computer engineering
Mohammad Sabri
1
,
Mohammad Moin
2
,
Farbod Razzazi
3
1 -
2 -
3 -
Keywords: Identity authenticationdeep learningmatch on cardmultibiometrics,
Abstract :
Nowadays, the threats such as terrorism and cybercrime are extremely increased, therefore, the identity authentication process is very substantial for the national security of a country. In this paper, we propose a novel multimodal authentication system with sequential structure based on deep learning. In the proposed method, feature vectors are extracted automatically through deep network with an end to end architecture. A multi biometric system using two fingerprint and a face is implemented and evaluated. The results demonstrate that, the authentication is done by fingerprints in 91.42% cases and only for 8.58% cases the face modal is required. In addition, the proposed method is more accurate than first and second fingerprint by 35% and 30% at FMR=0.001, respectively. As a result, we augmented the accuracy of the system and at the same time reduced the acquisition and matching time. This conducts to the improvement of user convenience and security of the service provider, simultaneously. The achievements of this work can be used to increase the effectiveness of authentication process and can play an important role in the acceptability of real world applications.
[1] K. D. Mitnick and W. L. Simon, The Art of Deception: Controlling the Human Element of Security, John Wiley & Sons, 2011.
[2] D. V. Klein, "Foiling the cracker: a survey of, and improvements to, password security," in Proc. of the 2nd USENIX Security Workshop, pp. 5-14, Berkeley, CA, USA, Aug. 1990.
[3] R. Brunelli and D. Falavigna, "Person identification using multiple cues," Pattern Analysis and Machine Intelligence, IEEE Trans. on, vol. 17, no. 10, pp. 955-966, Apr. 1995.
[4] A. K. Jain, et al., "Integrating faces, fingerprints, and soft biometric traits for user recognition," in Proc. ECCV Workshop BioAW, pp. 259-269 Prague, Czech Republic, 15-15 May 2004.
[5] R. Bolle, S. Pankanti, and A. K. Jain, Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society, Kluwer Academic Publishers, 2006.
[6] K. Nandakumar, Multibiometric Systems: Fusion Strategies and Template Security, Michigan State, p. 250, 2008.
[7] D. A. Cooper, et al., Interfaces for Personal Identity Verification, (including updates as of 02-08-2016) 2016.
[8] C. L. Wilson, P. J. Grother, and R. Chandramouli, Biometric data specification for personal identity verification, 2006.
[9] D. Isenor and S. G. Zaky, "Fingerprint identification using graph matching," Pattern Recognition, vol. 19, no. 2, pp. 113-122, Jan. 1986.
[10] ISO/IEC 19794-2:2011, Information Technology-Biometric Data Interchange Formats-Part 2: Finger Minutiae Data, 2011.
[11] P. J. Grother and W. J. Salamon, MINEX II Performance of Fingerprint Match-on-Card Algorithms-Phase II/III Report, 2009.
[12] M. Sabri, M. S. Moin, and F. Razzazi, "A new framework for match on card and match on host quality based multimodal biometric authentication," J. of Signal Processing Systems, vol. 91, no. 2, pp. 163-177, Feb. 2018 2018.
[13] N. Poh, T. Bourlai, and J. Kittler, "A multimodal biometric test bed for quality-dependent, cost-sensitive and client-specific score-level fusion algorithms," Pattern Recognition, vol. 43, no. 3, pp. 1094-1105, Mar. 2010.
[14] M. Vatsa, et al., "On the dynamic selection of biometric fusion algorithms," IEEE Trans. on Information Forensics and Security, vol. 5, no. 3, pp. 470-479, Sept. 2010.
[15] M. A. Olsen, V. Smida, and C. Busch, "Finger image quality assessment features-definitions and evaluation," IET Biometrics, vol. 5, no. 2, pp. 47-64, May 2016.
[16] S. Bharadwaj, M. Vatsa, and R. Singh, "Biometric quality: a review of fingerprint, iris, and face," EURASIP J. on Image and Video Processing, vol. 2014, no. 1, pp. 34-62, Jul. 2014.
[17] L. Best-Rowden and A. K. Jain, Automatic Face Image Quality Prediction, arXiv preprint arXiv:1706.09887, 2017.
[18] J. Chen, et al., "Face image quality assessment based on learning to rank," IEEE Signal Processing Letters, vol. 22, no. 1, pp. 90-94, Aug. 2015.
[19] C. S. Mlambo and M. B. Shabalala, "Distortion analysis on binary representation of minutiae based fingerprint matching for match-on-card," in Proc. IEEE Symp. Series on Computational Intelligence, pp. 349-353, Cape Town, South Africa, 7-10 Dec. 2015.
[20] M. Govan and T. Buggy, "A computationally efficient fingerprint matching algorithm for implementation on smartcards," in Proc. First IEEE Int. Conf. on Biometrics: Theory, Applications, and Systems, BTAS, 6 pp., Crystal City, VA, USA, 27-29 Sept. 2007.
[21] B. Vibert, C. Rosenberger, and A. Ninassi, "Security and performance evaluation platform of biometric match on card," in Proc. IEEE World Congress on Computer and Information Technology, WCCIT'13, 6 pp., Sousse, Tunisia, 22-24 Jun. 2013.
[22] K. K. Nair, A. Helberg, and J. Van der Merwe, "An approach to improve the match-on-card fingerprint authentication system security," in Proc. IEEE 6th Int. Conf. on Digital Information and Communication Technology and Its Applications, DICTAP'16, pp. 119-125, Konya, Turkey, 21-23 Jul. 2016.
[23] A. Nagar, K. Nandakumar, and A. K. Jain, "Biometric template transformation: a security analysis," Media Forensics and Security II. 2010. International Society for Optics and Photonics.
[24] A. Lumini and L. Nanni, "Overview of the combination of biometric matchers," Information Fusion, , vol. 33, no. 4, pp. 71-85, Jan. 2017.
[25] R. Raghavendra, et al., "Designing efficient fusion schemes for multimodal biometric systems using face and palmprint," Pattern Recognition, vol. 44, no. 5, pp. 1076-1088, May 2011.
[26] M. Vatsa, et al., "On the dynamic selection of biometric fusion algorithms," IEEE Trans. on Information Forensics and Security, vol. 5, no. 3, pp. 470-479, Jul. 2010.
[27] A. Baig, et al., "Cascaded multimodal biometric recognition framework," IET Biometrics, vol. 3, no. 1, pp. 16-28, Aug. 2013.
[28] S. Bharadwaj, et al., "QFuse: online learning framework for adaptive biometric system," Pattern Recognition, vol. 48, no. 11, pp. 3428-3439, Aug. 2015.
[29] D. Peralta, et al., "On the use of convolutional neural networks for robust classification of multiple fingerprint captures," International J. of Intelligent Systems, vol. 33, no. 1, pp. 213-230, Jan. 2018.
[30] H. U. Jang, et al., "DeepPore: fingerprint pore extraction using deep convolutional neural networks," IEEE Signal Processing Letters, vol. 24, no. 12, pp. 1808-1812, Oct. 2017.
[31] K. Simonyan and A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv preprint arXiv:1409.1556, 2014.
[32] Y. Tang, F. Gao, and J. Feng, Latent Fingerprint Minutia Extraction Using Fully Convolutional Network, arXiv preprint arXiv:1609.09850, 2016.
[33] Y. Tang, et al., FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction, arXiv preprint arXiv:1709.02228, 2017.
[34] H. Qin and M. A. El Yacoubi, "Deep representation for finger-vein image quality assessment," IEEE Trans. on Circuits and Systems for Video Technology, vol. 28, no. 8, pp. 1677-1693, Mar. 2017.
[35] F. Marra, et al., "A deep learning approach for iris sensor model identification," Pattern Recognition Letters, vol. 113, no. 1, pp. 46-53, Oct. 2017.
[36] D. Menotti, et al., "Deep representations for iris, face, and fingerprint spoofing detection," IEEE Trans. on Information Forensics and Security, vol. 10, no. 4, pp. 864-879, Feb. 2015.
[37] R. F. Nogueira, R. de Alencar Lotufo, and R. C. Machado, "Fingerprint liveness detection using convolutional neural networks," IEEE Trans. on Information Forensics and Security, vol. 11, no. 6, pp. 1206-1213, Jan. 2016.
[38] T. Chugh, K. Cao, and A. K. Jain, "Fingerprint spoof buster: use of minutiae-centered patches," IEEE Trans. on Information Forensics and Security, vol. 13, no. 9, pp. 2190-2202, Mar. 2018.
[39] L. Best-Rowden and A. K. Jain, "Learning face image quality from human assessments," IEEE Trans. on Information Forensics and Security, vol. 13, no. 12, pp. 3064-3077, Jan. 2018.
[40] ICAO, D., 9303-Machine Readable Travel Documents-Part 9: Deployment of Biometric Identification and Electronic Storage of Data in eMRTDs, International Civil Aviation Organization (ICAO), 2015,
[41] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in Neural Information Processing Systems, vol. 1, no. 1, pp. 1097-1105, Dec. 2012.
[42] X. Glorot and Y. Bengio, "Understanding the difficulty of training deep feedforward neural networks," in Proc. of the 13th International Conf. on Artificial Intelligence and Statistics, vol. 9, pp. 249-256, Sardinia, Italy, 13-15 May 2010.
[43] L. Bottou, "Large-scale machine learning with stochastic gradient descent," in Proc. of 19th Int. Symp. on Computational Statistics, COMPSTAT'10, pp. 177-186, Paris, France, 22-27 Aug. 2010.
[44] I. Sutskever, et al., "On the importance of initialization and momentum in deep learning," in Proc. Int Conf. on Machine Learning, ICML'13, vol. 28, pp. 1139-1147, Jun. 2013.
[45] A. Karpathy, Class Notes for Cs231n: Convolutional Neural Networks for Visual Recognition, Dept. of Comp. Sci., Stanford University, Palo Alto, CA, USA, spring 2017. [Online]. Available: http://cs231n.github.io/python-numpy-tutorial/
[46] C. I. Watson, et al., Fingerprint Vendor Technology Evaluation, 2012, NIST, NIST Interagency/Internal Report (NISTIR), Jan. 2015.
[47] M. S. Hossain and K. A. Rahman, "An empirical study on verifier order selection in serial fusion based multi-biometric verification system," in Proc. Int. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 249-258, Arras, France, 27-30 Jun. 2017.
[48] A. Jain, K. Nandakumar, and A. Ross, "Score normalization in multimodal biometric systems," Pattern Recognition, vol. 38, no. 12, pp. 2270-2285, Dec. 2005.
[49] C. W. Hsu, C. C. Chang, and C. J. Lin, A Practical Guide to Support Vector Classification, Technical Report, Department of Computer Science, National Taiwan University,Taipei, 2003.
[50] A. G. Howard, et al., Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications, arXiv preprint arXiv:1704.04861, 2017.
