SavedAdd your journal

Journals / Mühendislik Bilimleri Dergisi / 2007 / Cilt: 13 - Sayı: 1

Minutiae extraction based on artificial neural networks for automatic fingerprint recognition systems

Otomatik parmakizi tanıma sistemlerinde özellik noktalarının tespitinde yapay sinir ağlarının kullanılması

Necla ÖZKAYA
Şeref SAĞIROĞLU
Pages
91–101
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results have shown that artificial neural networks achieve the minutiae extraction from fingerprint images with high accuracy.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Automatic fingerprint recognition systems are utilised for personal identification with the use of comparisons of local ridge characteristics and their relationships.
📊
Key Findings & Data:Critical stages in personal identification are to extract features automatically, fast and reliably from the input fingerprint images.
💡
Conclusion & Impact:The results have shown that artificial neural networks achieve the minutiae extraction from fingerprint images with high accuracy.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

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.

Özet

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.

🔖

Cite This Article

ÖZKAYA, N., SAĞIROĞLU, Ş. (2007). Minutiae extraction based on artificial neural networks for automatic fingerprint recognition systems. Mühendislik Bilimleri Dergisi, Cilt: 13 - Sayı: 1, 91–101. https://ajindex.com/articles/291153

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (12)

  1. 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/.
  2. 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.
  3. Gonzalez, R.C., Woods, R. E. 1992. "Digital Image Processing", Third Edition,
  4. 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, L; 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.
  5. Jain. A.K., Hong, L., Bolle. R. 1997a,
  6. On-line Fingerprint Verification, IEEE Transactions on PAMI, 19 (4), 302-314.
  7. Jain. A. K., Hong. L. Pankanti, S., Bolle, R. 1997b. An Identity Authentication System Using Fingerprints, Proceedings of the IEEE. 85, 9, 1365-1388.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. Ongun, G. 1995. An