i-manager Publications

A Framework for Fingerprint Liveness Detection Using Support Vector Machine Optimized by Genetic Algorithm

Yusuf Ibrahim*, Muhammed B. Mu’azu**, Emmanuel A. Adedokun***, Yusuf A. Sha’aban****

*,**** Lecturer, Department of Computer Engineering, Ahmadu Bello University, Zaria, Nigeria.

** Professor and Head, Department of Computer Engineering, Ahmadu Bello University, Zaria, Nigeria.

*** Department of Electrical Engineering, Ahmadu Bello University, Zaria, Nigeria.

Periodicity:June - August'2018
DOI : https://doi.org/10.26634/jpr.5.2.15536

Abstract

Fingerprints are widely and successfully been used in a number of applications as a preferred biometric for personal identifications. However, current fingerprint authentication systems are vulnerable to direct spoof attacks at the sensor level as fake fingerprints artificially made to replicate genuine ones are now made using common materials such as silicone, gelatin, playdoh etc. This paper therefore implements a software based deep machine learning framework for classifying fingerprints images presented to the system as either been live or fake. Since typical fingerprint images are noisy, some preprocessing on the images were first of all performed using a decision based adaptive median filtering algorithm for de-noising and min-max normalization for enhancement. Features were then extracted using pre- trained Deep Convolutional Neural Network (DCNN) and their dimensionality reduced using Principal Component Analysis (PCA). Resulting features were then used to train a Support Vector Machine with Gaussian kernel optimized by Genetic Algorithm. The developed GA-SVM method was evaluated on the 3993 Biometrika datasets from the LivDet2009 database. The results obtained demonstrate robustness and effectiveness of the developed method in achieving good average liveness classification accuracy.

Keywords

liveness detection; Deep Convolutional Neural Networks; Support Vector Machines; Genetic Algorithm; classification.

How to Cite this Article?

Ibrahim, Y., Mu’azu, M. B., Adedokun, E. A.,and Sha’aban, Y. A (2018). A Framework for Fingerprint Liveness Detection Using Support Vector Machine Optimized by Genetic Algorithm. i-manager’s Journal on Pattern Recognition, 5(2), 1-9. https://doi.org/10.26634/jpr.5.2.15536

References

[1]. Abhyankar, A., & Schuckers, S. (2009). Integrating a wavelet based perspiration liveness check with fingerprint recognition. Pattern Recognition, 42(3), 452-464.

[2]. Antonelli, A., Cappelli, R., Maio, D., & Maltoni, D. (2006a). Fake finger detection by skin distortion analysis. IEEE Transactions on Information Forensics and Security, 1(3), 360-373.

[3]. Antonelli, A., Cappelli, R., Maio, D., & Maltoni, D. (2006b, January). A new approach to fake finger detection based on skin distortion. In International Conference on Biometrics (pp. 221-228). Springer, Berlin, Heidelberg.

[4]. Ardjani, F., Sadouni, K., & Benyettou, M. (2010, November). Optimization of SVM multiclass by particle swarm (PSO-SVM). In Database Technology and Applications (DBTA), 2010 2^nd International Workshop on (pp. 1-4). IEEE.

[5]. Baldisserra, D., Franco, A., Maio, D., & Maltoni, D. (2006, January). Fake fingerprint detection by odor analysis. In International Conference on Biometrics (pp. 265-272). Springer, Berlin, Heidelberg.

[6]. Chang, C-C., & Lin, C-J. (2012). A Library for Support Vector Machines [EB/Ol]. Retrieved from https://www. csie.ntu.edu.tw/~cjlin/libsvm/

[7]. Chatfield, K., Simonyan, K., Vedaldi, A., & Zisserman, A. (2014). Return of the devil in the details: Delving deep into convolutional nets. arXiv preprint arXiv:1405.3531.

[8]. Choi, H., Kang, R., Choi, K., Jin, A. T. B., & Kim, J. H. (2009). Fake-fingerprint detection using multiple static features. Optical Engineering, 48(4), 047202.

[9]. Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009, June). Imagenet: A large-scale hierarchical image database. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on (pp. 248-255). IEEE.

[10]. Dorian, C., Lefort, R., Bonnel, J., Zarader, J. L., & Adam, O. (2017). Bi-class classification of humpback whale sound units against complex background noise with Deep Convolution Neural Network. arXiv preprint arXiv:1703.10887.

[11]. Espinoza, M., & Champod, C. (2011, November). Using the number of pores on fingerprint images to detect spoofing attacks. In Hand-Based Biometrics (ICHB), 2011 International Conference on (pp. 1-5). IEEE.

[12]. Galbally, J., Alonso-Fernandez, F., Fierrez, J., & Ortega-Garcia, J. (2012). A high performance fingerprint liveness detection method based on quality related features. Future Generation Computer Systems, 28(1), 311-321.

[13]. Ghiani, L., Hadid, A., Marcialis, G. L., & Roli, F. (2013, September). Fingerprint liveness detection using binarized statistical image features. In Biometrics: Theory, Applications and Systems (BTAS), 2013 IEEE Sixth International Conference on (pp. 1-6). IEEE.

[14]. Ghiani, L., Marcialis, G. L., & Roli, F. (2012, November). Fingerprint liveness detection by local phase quantization. In Pattern Recognition (ICPR), 2012 21^st International Conference on (pp. 537-540). IEEE.

[15]. Ibrahim, Y., Mu'azu, M. B., Adedokun, A. E., & Sha'aban, Y. A. (2017). A performance analysis of logistic regression and Support Vector Machine classifiers for Spoof Fingerprint Detection. IEEE 3^rd International Conference on Electro-Technology for National Development (NIGERCON) (pp. 1-5).

[16]. Jia, J., Cai, L., Zhang, K., & Chen, D. (2007, August). A new approach to fake finger detection based on skin elasticity analysis. In International Conference on Biometrics (pp. 309-318). Springer, Berlin, Heidelberg.

[17]. Jiang, Y., & Liu, X. (2015). Spoof fingerprint detection based on co-occurrence matrix. International Journal of Signal Processing, Image Processing and Pattern Recognition, 8(8), 373-384.

[18]. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (pp. 1097-1105).

[19]. Lapsley, P. D., Lee, J. A., Pare Jr, D. F., & Hoffman, N. (1998). U.S. Patent No. 5,737,439. Washington, DC: U.S. Patent and Trademark Office.

[20]. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436.

[21]. Lee, H. S., Maeng, H. J., & Bae, Y. S. (2009, September). Fake finger detection using the fractional Fourier transform. In European Workshop on Biometrics and Identity Management (pp. 318-324). Springer, Berlin, Heidelberg.

[22]. Li, X. Z., & Kong, J. M. (2014). Application of GA–SVM method with parameter optimization for landslide development prediction. Natural Hazards and Earth System Sciences, 14(3), 525-533.

[23]. Manivanan, N., Memon, S., & Balachandran, W. (2010). Automatic detection of active sweat pores of fingerprint using highpass and correlation filtering. Electronics Letters, 46(18), 1268-1269.

[24]. Marasco, E., & Sansone, C. (2012). Combining perspiration-and morphology-based static features for fingerprint liveness detection. Pattern Recognition Letters, 33(9), 1148-1156.

[25]. Memon, S. A. (2012). Novel active sweat pores based liveness detection techniques for fingerprint biometrics (Doctoral Dissertation, Brunel University School of Engineering and Design).

[26]. Nikam, S. B., & Agarwal, S. (2009). Ridgelet-based fake fingerprint detection. Neurocomputing, 72(10-12), 2491-2506.

[27]. Sasikala, V., & Prabha, V. L. (2016). A Fuzzy based classification approach for efficient fake and real fingerprint classification with intelligent feature selection. WSEAS Transactions on Computers, 15, 143-157.

[28]. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013). Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229.

[29]. Sherin, B. M., & Supriya, M. H. (2015, November). GA based selection and parameter optimization for an SVM based underwater target classifier. In Ocean Electronics (SYMPOL), 2015 International Symposium on (pp. 1-7). IEEE.

[30]. Sukawattanavijit, C., Chen, J., & Zhang, H. (2017). GA-SVM algorithm for improving land-cover classification using SAR and optical remote sensing data. IEEE Geoscience and Remote Sensing Letters, 14(3), 284-288.

[31]. Syarif, I., Prugel-Bennett, A., & Wills, G. (2016). SVM parameter optimization using grid search and genetic algorithm to improve classification performance. TELKOMNIKA (Telecommunication Computing Electronics and Control), 14(4), 1502-1509.

[32]. Wang, C., Li, K., Wu, Z., & Zhao, Q. (2015, November). A DCNN based fingerprint liveness detection algorithm with voting strategy. In Chinese Conference on Biometric Recognition (pp. 241-249). Springer, Cham.

[33]. Xiao, T., Ren, D., Lei, S., Zhang, J., & Liu, X. (2014, June). Based on grid-search and PSO parameter optimization for Support Vector Machine. In Intelligent Control and Automation (WCICA), 2014 11^th World Congress on (pp. 1529-1533). IEEE.

[34]. Yambay, D., Ghiani, L., Denti, P., Marcialis, G. L., Roli, F., & Schuckers, S. (2012, March). LivDet 2011-Fingerprint liveness detection competition 2011. In Biometrics (ICB), 2012 5^th IAPR International Conference on (pp. 208-215). IEEE.

[35]. Zeiler, M. D., & Fergus, R. (2014, September). Visualizing and understanding convolutional networks. In European Conference on Computer Vision (pp. 818- 833). Springer, Cham.

A Framework for Fingerprint Liveness Detection Using Support Vector Machine Optimized by Genetic Algorithm

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