Parmak İzinden Cinsiyet Tahmini İçin Yeni Bir Zeki Sistem

Bu makale, sadece parmak izlerinden cinsiyetlerin tahmin edilmesi için Yapay Sinir Ağları (YSA) modeli tabanlı yeni bir metod önermektedir. Modelleme işlemleri parmak izlerinin alınması, elde edilen bir parmak izi bölümünün (örneğin 5x5mm) farklı boyutlarının analiz edimesi, köşegen çizgisinin çizilmesi, köşegen çizgisi üzerindeki tepe çizgilerinin otomatik belirlenmesi, tepe sayılarının hesaplanması, tepe kalınlığı ve bireylerin parmakizi ortalama tepe sayılarının bulunması, bu özniteliklerin bir model içerisinde birleştirilmesi, bunun yapısının hazırlanması ve ayarlanması ve son olarak modelin test edilmesi ile elde edilmektedir. Önerilen modelin sonuçları en iyi modelin %72 başarıyla elde edildiğini göstermektedir.

Anahtar Kelimeler:

Biyometri, Parmak izi, Cinsiyet tahmini, Zeki sistem, Tepe yoğunluğu

A New Intelligent System for Predicting Gender from Fingerprint

This paper proposes a new method for predicting genders from only fingerprints based on Artificial Neural Network (ANN) model. The modelling tasks are achieved by capturing fingerprints, analyzing an obtained fingerprint part (for example cropping 5x5mm part from a fingerprint image), determining ridges crossed by the diagonal of the obtained fingerprint part automatically, finding the ridge counts, ridge thicknesses, average fingerprint ridge counts of individuals, combining these features in a model, preparing and setting the structure of it and finally testing the model. The results of proposed model have shown that the best model achieves the task within 72% accuracy.

Keywords:

Biometrics, Fingerprint, Gender prediction, Intelligent system, Ridge density,

PDF

___

[1] N. Özkaya, and Ş. Sağıroğlu, “Public Key Infrastructure and Biometric Systems”, 1st National Symposium on Electronic Signatures Proceeding Book, Ankara, Turkey, 2006, pp. 283-290.
[2] S. Görgünoğlu and A. Çavuşoğlu, “Performance Analysis of Feature Extraction Algorithms Used in Fingerprint Recognition Systems”, 5th International Symposium on Advanced Technologies (IATS’09) Proceeding Book, Karabük, Turkey, 2009, pp. 104-107.
[3] V.V. Nabiyev, M. Ekinci and Y. Öztürk, “Biometric Scanning by Palm Lines”, 10th National Electrical, Electronics and Computer Engineering Congress and Expo Proceeding Book, İstanbul, Turkey, 2005, pp. 535-538.
[4] M. Yozgat, “Fingerprint Recognition on Computer”, MSc Thesis, Institute of Science and Technology, Gazi University, Ankara, 2003.
[5] G. Dede and M.H. Sazlı, “Examination of Biometric Systems from the Perspective of Pattern Recognition and Voice Recognition Module Simulation”, EMO 13th National Congress Proceeding Book, Ankara, Turkey, 2009, pp. 57-61.
[6] A. Ross and A.K. Jain, “Information Fusion in Biometrics”, Pattern Recognition Letters, vol. 24, no.13, pp. 2115-2125, 2003.
[7] O. Urhan, M.K. Güllü and S. Ertürk, “Modified Phase-Correlation Based Robust Hard-Cut Detection with Application to Archive Film”, IEEE Transactions on Circuits and Systems for Video Technology, vol. 6, pp. 753-770, 2006.
[8] M.M. Karabulut, “Fingerprint Recognition Based Real-Time Student Attendance System Automation”, MSc Thesis, Institute of Science and Technology, Fırat University, Elazığ, 2010.
[9] E.B. Sönmez, N.Ö. Özbek and Ö. Özbek, “Palm Print and Fingerprint-Based Biometric Identification System”, Academic Computing’08 Proceeding Book, Çanakkale, Turkey, 2008, pp. 577-
[10] A.K. Jain, L. Hong, S. Pankanti and R. Bolle, “An Identity-Authentication System Using Fingerprints”, Proceedings of the IEEE, vol. 85, no. 9, pp. 1365-1388, 1997.
[11] S. Singh, “2D Spiral Pattern Recognition with Possibilistic Measures”, Pattern Recognition Letters, vol. 19, pp. 141-147, 1998.
[12] N. Özkaya, Ş. Sağıroğlu and A. Wani, “An Intelligent Automatic Fingerprint Recognition System Design”, International Conference on Machine Learning and its Applications, Orlando, USA, 2006, pp. 231-238.
[13] N.K. Ratha, A. Jain and D.T. Rover, Fingerprint Matching on Splash 2, in: Buell D, Arnold J, Kleinfolder W (Eds.), MI, USA: IEEE Computer Society Press, pp. 117-140, 1996.
[14] A.K. Jain, K. Karu, S. Chen and N.K. Ratha, “A Real Time Matching System for Large Fingerprint Databases”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no.8, pp. 799-813, 1996.
[15] E. Gutierrez-Redomero, C. Alonso, E. Romero and V. Galera, “Variability of Fingerprint Ridge Density in a Sample of Spanish Caucasians and Its Application to Sex Determination”, Forensic Science International, vol. 180, pp. 17-22, 2008.
[16] M.A. Acree, “Is There a Gender Difference in Fingerprint Ridge Density?”, Forensic Science International, vol. 102, pp. 35-44, 1999.
[17] V.C. Nayak, P. Rastogi, T. Kanchan, K. Yoganarasimha, G.P. Kumar and R.G. Menezes, “Sex Differences from Fingerprint Ridge Density In Chinese and Malaysian Population”, Forensic Science International, vol. 197, pp. 67- 69, 2010.
[18] E. Gutierrez-Redomero, M.C. Alonso and J.E. Dipierri, “Sex Differences in Fingerprint Ridge Density in the Mataco-Mataguayo Population”, Journal of Comparative Human Biology, vol. 62, pp. 487-499, 2011.
[19] M.D. Nithin, B. Manjunatha, D.S. Preethi and B.M. Balaraj, “Gender Differentiation by Finger Ridge Count Among South Indian Population”, Journal of Forensic and Legal Medicine, vol. 18, pp. 79- 81, 2011.
[20] R.T. Moore, Automatic Fingerprint Identification Systems, Boca Raton, USA: CRC Press, 1994, pp. 164-191.
[21] N. Özkaya, “Associating Facial and Fingerprint Biometric Features with Flexible Computing Methods”, PhD Dissertation, Institute of Science and Technology, Erciyes University, Kayseri, 2009.
[22] Cross Validation – Statistics. (2018, February 1). [Online]. Available: http://en.wikipedia.org/wiki/Cross-validation_(statistics).
[23] S. Gungadin, “Sex Determination from Fingerprint Ridge Density”, Internet Journal of Medical Update, vol. 2, no. 2, pp. 4-7, 2007.
[24] E.B. Ceyhan, Ş. Sağıroğlu and E. Akyıl, “Statistical Gender Analysis Based on Fingerprint Ridge Density”, In Proceedings of IEEE Signal Processing Applications, Girne, Cyprus, 2013, pp. 472.
[25] E.B. Ceyhan, “Intelligent System for Identifying Gender from Fingerprint”, MSc Thesis, Institute of Science and Technology, Gazi University, Ankara, 2012.
[26] K.S. Arun and K.S. Sarath, “A Machine Learning Approach for Fingerprint Based Gender Identification”, IEEE Recent Advances in Intelligent Computational Systems, India, 2011, pp. 163-167.
[27] K. Nandakumar, A.K. Jain and S. Pankanti, “Fingerprint-based fuzzy vault: Implementation and Performance”, IEEE Transactions on Information Forensics and Security, vol. 2, no. 4, pp. 744-757, 2007.
[28] E.B. Ceyhan, S. Sagiroglu and E. Akyil, “Parmak İzi Öznitelik Vektörleri Kullanılarak YSA Tabanlı Cinsiyet Sınıflandırma”, Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, vol. 29, no. 1, pp. 201-207, 2014.
[29] S. Sagiroglu, E.B. Ceyhan, U. Yavanoglu and E. Akyil, “Parmak İzinden Cinsiyet Tanıyan Zeki Sistem”, Turk Patent. TPE 2012/07018, April 21, 2015.
[30] S. Sagiroglu, E.B. Ceyhan, U. Yavanoglu and E. Akyil, “System for Estimating Gender from Fingerprints”, European Patent, PCT/TR2013/000185, March 20, 2014.
[31] S. Sagiroglu, E.B. Ceyhan, U. Yavanoglu and E. Akyil, “System for estimating gender from fingerprints”, United States Patent, US9378406B2, June 06, 2016.