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OTOMATİK PARMAKİZİ TANIMA SİSTEMLERİNDE ÖZELLİK NOKTALARININ TESPİTİNDE YAPAY SİNİR AĞLARININ KULLANILMASI

MINUTIAE EXTRACTION BASED ON ARTIFICIAL NEURAL NETWORKS FOR AUTOMATIC FINGERPRINT RECOGNITION SYSTEMS

Journal Name:

  • Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi

Publication Year:

  • 2007

Key Words:

Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
Automatic fingerprint recognition systems are utilised for personal identification with the use of comparisons of local ridge characteristics and their relationships. Critical stages in personal identification are to extract features automatically, fast and reliably from the input fingerprint images. In this study, a new approach based on artificial neural networks to extract minutiae from fingerprint images is developed and introduced. The results have shown that artificial neural networks achieve the minutiae extraction from fingerprint images with high accuracy.
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Abstract (Original Language): 
Otomatik parmakizi tanıma sistemleriyle kimliklendirme yapılırken, özellik noktaları olarak bilinen parmakizi resimlerindeki hat çizgisi karakteristiklerinden ve bunların birbirleriyle olan ilişkilerinden faydalanılır. Bu yüzden giriş parmakizi resminden özellik noktalarının sorunsuz, güvenilir, hızlı ve otomatik olarak elde edilebilmesi kimliklendirme için çok önemlidir. Bu çalışmada, parmakizi tanımada kullanılan özellik noktalarının tespit edilmesine yönelik yapay sinir ağları temelli yeni bir yaklaşım geliştirilmiş ve sunulmuştur. Elde edilen sonuçlar parmakizi resminde özellik noktalarının bulunmasında yapay sinir ağlarının başarılı olduğunu göstermiştir.
FULL TEXT (PDF): 
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  • Turkish

REFERENCES

References: 

Alkaya, E. 1998. Enhancement and Preprocessing Techniques For Ridge Extraction in Fingerprint Images, Yüksek Lisans Tezi, Orta Doğu Teknik Üniversitesi, Ankara.
Anonymous, 2006. Matlab 7.0.1. Neural Network Toolbox User's Guide, 2006,
http://www.mathworks.com/ access/helpdesk/ help/toolbox/nnet/.
Bhanu B., Boshra, M., Xuejun, T. 2000. Logical
Templates For Feature Extraction in Fingerprint Images, 15th International Conference on Pattern Recognition, (2) 846 -850.
Espinosa-Duro, V. 2002. Minutiae Detection Algorithm For Fingerprint Recognition, IEEE Aerospace & Electronics Systems Magazine, (17) 7-10.
Gonzalez, R.C., Woods, R. E. 1992. "Digital Image Processing", Third Edition, Addison-Wesley, Reading, MA.
Greenberg, S., Aladjem, M., Kogan, D.,
Dimitrov
, I. 2000. Fingerprint Image Enhancement Using Filtering Techniques, 15th International Conference on Pattern Recognition, (3) 322 -325.
Halici
U.
, Jain, L. C., Erol, A. 1999. "Introduction to Fingerprint Recognition" Chapter in Jain L. C.; Halici U.; Hayashi, I.; Lee, S.B.; Tsutsui T., 1999, Intelligent Biometric Techniques in Fingerprint and Face Recognition, CRC press, USA.
Hong, L., Wan, Y., Jain, A.K. 1998. Fingerprint Image Enhancement: Algorithms and Performance Evaluation, IEEE Transactions on PAMI , 20, 8,
777-789.
Jain, A. K., Hong, L., Bolle, R. 1997a. On-line
Fingerprint Verification, IEEE Transactions on PAMI, 19 (4), 302-314.
Jain, A. K., Hong, L., Pankanti, S., Bolle, R. 1997b.
An Identity Authentication System Using Fingerprints, Proceedings of the IEEE, 85, 9, 1365¬1388.
Jain, A. K., Prabhakar, S., Hong, L., Pankanti, S. 1999. FingerCode: A Filterbank For Fingerprint Representation and Matching, IEEE Computer Society Conference on Computer Vision and Pattern
Recognition, (2)193-199.
Jin, A. L. H., Chekima, A., Dargham, J. A., Liau C. F. 2002. Fingerprint Identification and Recognition Using Backpropagation Neural Network, Student Conference on Research and Development
SCOReD, 98-101.
Liu, J., Huang, Z., Chan, K. L. 2000. Direct Minutiae Extraction From Grey-level Fingerprint Image by Relationship Examination, International Conference on Image Processing, 2, 427-430.
Luo, X., Tian, J., Wu, Y. 2000. A Minutiae Matching Algorithm in Fingerprint Verification, 15th International Conference on Pattern Recognition, 4, 833 -836.
Maio, D., Maltoni, D. 1998. Neural Network Based Minutiae Filtering in Fingerprints Fourteenth International Conference on Pattern Recognition, 2, 1654 -1658.
Ongun, G. 1995. An Automatic Fingerprint Identification System Based On Self organizing Feature Maps Classifier, Yüksek Lisans Tezi, Orta Doğu Teknik Üniversitesi, Ankara, Türkiye.
Özkaya,
N
. 2003. Otomatik Parmakizi Tanıma Sistemi, Yüksek Lisans Tezi, Fen Bilimleri Enstitüsü, Erciyes Üniversitesi, Kayseri, Türkiye.
Rusyn, B., Prudyus, I., Ostap, V. 2001. Fingerprint Image Enhancement Algorithm, The Experience of Designing and Application of CAD Systems in Microelectronics CADSM, 193-194.
Saatci, E., Tavsanoglu, V. 2002. Fingerprint Image Enhancement Using CNN Gabor-type filters, 7th
Mühendislik Bilimleri Dergisi 2007 13 (1) 91-101
100
Journal of Engineering Sciences 2007 13 (1) 91-101
Otomatik Parmakizi Tanıma Sistemlerinde Özellik Noktalarının Tespitinde Yapay Sinir Ağlarının Kullanılması, N. Özkaya, Ş. Sağıroğlu
IEEE International Workshop on Cellular Neural Networks and Their Applications (CNNA), 377-382.
Sagar, V. K.,
Beng
, K. J. A. 1999a. Hybrid Fuzzy Logic and Neural Network Model for Fingerprint Minutiae Extraction, International Joint Conference on Neural Networks IJCNN '99, (5) 3255 -3259.
Sagar, V. K., Beng, K. J. A. 1999b. Fingerprint Feature Extraction by Fuzzy Logic and Neural
Networks, 6th International Conference on Neural Information Processing ICONIP'99, (3) 1138 -1142.
Sağıroğlu, Ş., Beşdok, E., Erler, M. 2003. Mühendislikte Yapay Zeka Uygulamaları I: Yapay Sinir Ağları, Ufuk Kitabevi, Türkiye.
Xiao, Q., Raafat, H. 1991. Fingerprint Image Post¬Processing: A Combined Statistical and Structural Approach, PR(24), (10) 985-992.

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