PARMAK İZİ TANIMA İÇİN FARKLI SINIFLANDIRICILARIN KARŞILAŞTIRMALI BAŞARIM ANALİZİ

Year 2018, Volume: 7 Issue: 2, 504 - 513, 20.07.2018

Alper Baştürk , Nurcan Sarıkaya Baştürk Orxan Qurbanov

https://doi.org/10.28948/ngumuh.443160

Cited By: 2

Abstract

Bu
çalışmada, güncel sınıflandırıcılar ve ayrıca literatürdeki mevcut bazı önemli ve
yaygın sınıflandırıcılar kullanılarak parmak izi görüntüleri tanınmıştır. Çalışmada
kullanılan sınıflandırma yöntemleri; destek vektör makineleri, k-en yakın
komşu, Naive-Bayes, karar ağacı öğrenimi ve derin sinir ağlarıdır. Eğitim ve
test veri setleri temel olarak 165 farklı parmağın dört farklı parmak izi
görüntüsü alınarak elde edilmiştir. Her bir farklı parmak izi görüntüsüne ek
olarak, bu izlerin yedi farklı döndürülmüş versiyonu da veri kümesini
genişletmek amacıyla kullanılmıştır. Her parmak izi görüntüsünün özellik
vektörü (parmak kodu), yönlü Gabor süzgeci ile süzgeçleme sonrası çıktı
görüntülerindeki ilgilenilen (sektör) alanlarının ortalaması alınarak
üretilmiştir. Parmak izi veri seti oluşturulduktan sonra, tüm sınıflandırıcılar
parmak izi görüntülerini tanımak üzere eğitilmiştir. Detaylı simülasyon çalışmaları,
parmak izi görüntülerinin tanınması amacıyla kullanılan sınıflandırıcılar
arasında en başarımlı olanının derin sinir ağı tabanlı sınıflandırıcı olduğunu
göstermiştir.

Keywords

Derin sinir ağı, parmak izi tanıma, sınıflandırma

A COMPARATIVE PERFORMANCE ANALYSIS OF VARIOUS CLASSIFIERS FOR FINGERPRINT RECOGNITION

Abstract

In
this study, recognition of fingerprint images has been performed by recent
classifiers as well as some important and common classifiers available in the
literature. The classification methods used in the study are support vector
machines, k-nearest neighbors, Naive-Bayes, decision tree learning, and deep
neural networks. Training/testing data set has been obtained basically by using
four different versions of fingerprint images of 165 different fingers.
Additional seven rotated versions of each different fingerprint images are also
used to extend the data set. Feature vector of each fingerprint image (a
fingercode) has been produced by using directional Gabor filters and averaging
specific regions (sectors) of their output images. After creating fingercode
data set, all classifiers has been trained to recognize fingerprint images. Detailed
simulation results show that deep neural networks can be effectively used among
all classifiers for recognition of fingerprint images.

Keywords

Deep neural networks, fingerprint recognition, classification

References

• [1] NALINI K. RATHA, R. BOLLE, Automatic fingerprint recognition systems. Springer, 2004.
• [2] Q. ZHANG, H. YAN, “Fingerprint classification based on extraction and analysis of singularities and pseudo ridges,” Pattern Recognit., vol. 37, no. 11, pp. 2233–2243, Nov. 2004.
• [3] J. LI, W.-Y. YAU, H. WANG, “Combining singular points and orientation image information for fingerprint classification,” Pattern Recognit., vol. 41, no. 1, pp. 353–366, Jan. 2008.
• [4] A. K. JAIN, S. PRABHAKAR, LIN HONG, “A multichannel approach to fingerprint classification,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 21, no. 4, pp. 348–359, Apr. 1999.
• [5] D. MAIO, D. MALTONI, “A structural approach to fingerprint classification,” in Proceedings of 13th International Conference on Pattern Recognition, 1996, pp. 578–585 vol.3.
• [6] R. CAPPELLI, A. LUMINI, D. MAIO, D. MALTONI, “Fingerprint classification by directional image partitioning,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 21, no. 5, pp. 402–421, May 1999.
• [7] A. SENIOR, “A combination fingerprint classifier,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 10, pp. 1165–1174, 2001.
• [8] B. MOAYER, K.-S. FU, “A Tree System Approach for Fingerprint Pattern Recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-8, no. 3, pp. 376–387, May 1986.
• [9] B. MOAYER, K. S. FU, “An application of stochastic languages to fingerprint pattern recognition,” Pattern Recognit., vol. 8, no. 3, pp. 173–179, Jul. 1976.
• [10] J.-H. CHANG, K.-C. FAN, “A new model for fingerprint classification by ridge distribution sequences,” Pattern Recognit., vol. 35, no. 6, pp. 1209–1223, Jun. 2002.
• [11] K. A. NAGATY, “Fingerprints classification using artificial neural networks: a combined structural and statistical approach,” Neural Networks, vol. 14, no. 9, pp. 1293–1305, Nov. 2001.
• [12] C. L. WILSON, G. T. CANDELA, C. I. WATSON, “Neural network fingerprint classification,” J. Artif. Neural Networks, vol. 1, no. 2, pp. 203–228, 1994.
• [13] WILSON, C. L., BLUE, J. L., OMIDVAR O. M., “Improving Neural Network Performance for Character and Fingerprint Classification by Altering Network Dynamics,” in Proceedings of theWorld Congress on Neural Networks, 1995, pp. 151–158.
• [14] M. KAMIJO, H. MIENO, K. KOJIMA, “Classification of fingerprint images using a neural network,” Syst. Comput. Japan, vol. 23, no. 3, pp. 89–101, 1992.
• [15] G. LUCA MARCIALIS, F. ROLI, P. FRASCONI, “Fingerprint Classification by Combination of Flat and Structural Approaches,” in International Conference on Audio-and Video-Based Biometric Person Authentication, Springer, Berlin, Heidelberg, 2001, pp. 241–246.
• [16] U. HALICI, G. ONGUN, “Fingerprint classification through self-organizing feature maps modified to treat uncertainties,” Proc. IEEE, vol. 84, no. 10, pp. 1497–1512, 1996.
• [17] K. A. NAGATY, “On learning to estimate the block directional image of a fingerprint using a hierarchical neural network,” Neural Networks, vol. 16, no. 1, pp. 133–144, Jan. 2003.
• [18] L. N. DARLOW, B. ROSMAN, “Fingerprint minutiae extraction using deep learning,” in 2017 IEEE International Joint Conference on Biometrics (IJCB), 2017, pp. 22–30.
• [19] D. MICHELSANTI, A.-D. ENE, Y. GUICHI, R. STEF, K. NASROLLAHI, T. B. MOESLUND, “Fast Fingerprint Classification with Deep Neural Networks,” in Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, 2017, pp. 202–209.
• [20] Y. WANG, Z. WU, J. ZHANG, “Damaged fingerprint classification by Deep Learning with fuzzy feature points,” in 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2016, pp. 280–285.
• [21] A. BASTURK, N. SARIKAYA BASTURK, O. QURBANOV, “Fingerprint Recognition by Deep Neural Networks and Fingercodes,” in IEEE 26th Signal Processing and Communications Applications Conference, 2018, pp. 1–4.
• [22] H. BADEM, A. BASTURK, A. CALISKAN, M. E. YUKSEL, “A new efficient training strategy for deep neural networks by hybridization of artificial bee colony and limited–memory BFGS optimization algorithms,” Neurocomputing, vol. 266, pp. 506–526, Nov. 2017.
• [23] A. CALISKAN, H. BADEM, A. BAŞTÜRK, M. E. YÜKSEL, “Diagnosis of the parkinson disease by using deep neural network classifier,” Istanbul Univ. - J. Electr. Electron. Eng., vol. 17, no. 2, pp. 3311–3318, 2017.
• [24] A. CALISKAN, M. E. YUKSEL, H. BADEM, A. BASTURK, “A Deep Neural Network Classifier for Decoding Human Brain Activity Based on Magnetoencephalography,” Elektron. ir Elektrotechnika, vol. 23, no. 2, pp. 63–67, Apr. 2017.
• [25] A. CALISKAN, M. E. YUKSEL, H. BADEM, A. BASTURK, “Performance improvement of deep neural network classifiers by a simple training strategy,” Eng. Appl. Artif. Intell., vol. 67, pp. 14–23, Jan. 2018.
• [26] D. E. RUMELHART, G. E. HINTON, R. J. WILLIAMS, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, Oct. 1986.
• [27] Y. LECUN, L. BOTTOU, Y. BENGIO, P. HAFFNER, “Gradient-based learning applied to document recognition,” Proc. IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.
• [28] R. RAINA, A. MADHAVAN, A. Y. NG, “Large-scale deep unsupervised learning using graphics processors,” in Proceedings of the 26th Annual International Conference on Machine Learning - ICML ’09, 2009, pp. 873–880.
• [29] T.-F. WU, C.-J. LIN, R. C. WENG, “Probability Estimates for Multi-class Classification by Pairwise Coupling,” J. Mach. Learn. Res., vol. 5, pp. 975–1005, 2004.
• [30] M. ARUN KUMAR, M. GOPAL, “Reduced one-against-all method for multiclass SVM classification,” Expert Syst. Appl., vol. 38, no. 11, pp. 14238–14248, Oct. 2011.
• [31] M. E. HELLMAN, “The Nearest Neighbor Classification Rule with a Reject Option,” IEEE Trans. Syst. Sci. Cybern., vol. 6, no. 3, pp. 179–185, Jul. 1970.
• [32] R. R. YAGER, “An extension of the naive Bayesian classifier,” Inf. Sci. (Ny)., vol. 176, no. 5, pp. 577–588, Mar. 2006.
• [33] L. ROKACH, O. MAIMON, Data Mining with Decision Trees, vol. 69. WORLD SCIENTIFIC, 2007.
• [34] M. RANZATO, C. POULTNEY, S. CHOPRA, Y. LECUN, “Efficient learning of sparse representations with an energy-based model,” Proceedings of the 19th International Conference on Neural Information Processing Systems. MIT Press, pp. 1137–1144, 2006.
• [35] Q. V LE, J. NGIAM, A. COATES, A. LAHIRI, B. PROCHNOW, A. Y. NG, “On Optimization Methods for Deep Learning,” in Proceedings of the 28th International Conference on International Conference on Machine Learning, 2011, pp. 265–272.
• [36] Y. BENGIO, “Practical Recommendations for Gradient-Based Training of Deep Architectures,” Springer, Berlin, Heidelberg, 2012, pp. 437–478.
• [37] S. TAO, T. ZHANG, J. YANG, X. WANG, W. LU, “Bearing fault diagnosis method based on stacked autoencoder and softmax regression,” in 2015 34th Chinese Control Conference (CCC), 2015, pp. 6331–6335.
• [38] Y. ZHANG, E. ZHANG, W. CHEN, “Deep neural network for halftone image classification based on sparse auto-encoder,” Eng. Appl. Artif. Intell., vol. 50, pp. 245–255, Apr. 2016.
• [39] M. F. MØLLER, “A scaled conjugate gradient algorithm for fast supervised learning,” Neural Networks, vol. 6, no. 4, pp. 525–533, Jan. 1993.