ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Reliable Orientation Field Estimation of Fingerprint Based on Adaptive Neighborhood Analysis


Affiliations
1 Department of Computer Science, Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women, India
     

   Subscribe/Renew Journal


Fingerprint Orientation estimation is an important step in feature extraction and classification. However, a reliable extraction of fingerprint orientation data is still a challenge for poor quality images. In this paper, a gradient based estimation of orientation field based on the analysis of orientation consistency in the neighborhood for regularizing the orientation field is proposed. Experimental results are analyzed and compared with other existing gradient based methods used in this work. Evaluation performed on standard FVC2002 fingerprint databases DB1, DB2 and sample fingerprint images collected using optical fingerprint reader exhibit visibly better orientation estimation for various quality images using the proposed method.

Keywords

Gradient-Based Method, Orientation Map, Gaussian Filter, Orientation Smoothing, Orientation Consistency.
Subscription Login to verify subscription
User
Notifications
Font Size

  • Davide Maltoni, Dario Maio, Anil K.Jain and Salil Prabhakar, “Handbook of Fingerprint Recognition”, 2nd Edition, Springer, 2009.

  • Manhua Liu, Xudong Jiang and Alex Chichung Kot, “Fingerprint Reference-Point Detection”, EURASIP Journal on Applied Signal Processing, Vol. 4, pp. 498-509, 2005.

  • Jinwei Gu, Jie Zhou and David Zhang, “A Combination Model for Orientation Field of Fingerprints”, Pattern Recognition, Vol. 37, No. 3, pp. 543-553, 2004.

  • Xiao Yang, Jianjiang Feng and Jie Zhou, “Localized Dictionaries Based Orientation Field Estimation for Latent Fingerprints”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 36, No. 5, pp. 955-969, 2014.

  • Francesco Turroni, Davide Maltoni, Raffaele Cappelli and Dario Maio, “Improving Fingerprint Orientation Extraction”, IEEE Transactions on Information Forensics And Security, Vol. 6, No. 3, pp. 1002-1013, 2011.

  • Iwasokun Gabriel Babatunde, Akinyokun Oluwole Charles and Olabode Olatubosun, “A Block Processing Approach to Fingerprint Ridge-Orientation Estimation”, Computer Technology and Application, Vol. 3, pp. 401-407, 2012.

  • Jayant V. Kulkarni, Bhushan D. Patil and Raghunath S. Holambe, “Orientation Feature for Fingerprint Matching”, Pattern Recognition, Vol. 39, No. 8, pp. 1551-1554, 2006.

  • Ravishankar Rao and Brian G. Schunck, “Computing Oriented Texture Fields”, Graphical Models and Image Processing, Vol. 53, No. 2, pp. 157-185, 1991.

  • Nalini K. Ratha, Shaoyun Chen and Anil K. Jain, “Adaptive Flow Orientation-Based Feature Extraction in Fingerprint Images”, Pattern Recognition, Vol. 28, No. 11, pp. 1657-1672, 1995.

  • Asker M. Bazen and Sabih H. Gerez, “Systematic Methods for the Computation of the Directional Fields and Singular Points of Fingerprints”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, pp. 905-919, 2002.

  • H.B Kekre and V.A Bharadi, “Fingerprint Orientation Field Estimation Algorithm based on Optimized Neighborhood Averaging”, Proceedings of 2ndInternational Conference on Emerging Trends in Engineering and Technology, pp. 228-234, 2009.

  • Manhua Liu, Xudong Jiang and Alex Chichung Kot, “Nonlinear Fingerprint Orientation Smoothing by Median Filter”, Proceedings of International Conference on Information Communications and Signal Processing, pp. 1439-1443, 2005.

  • Michael Kaas and Andrew Witkin, “Analyzing Oriented pattern”, Computer Vision, Graphics, and Image Processing, Vol. 37, No. 3, pp. 362-385, 1987.

  • Mikel Galar et al., “A Survey of Fingerprint Classification Part II: Experimental Analysis and Ensemble Proposal”, Knowledge-Based Systems, Vol. 81, pp. 98-116, 2015.

  • R. Cappelli, A. Lumini, D. Maio and D. Maltoni, “Fingerprint Classification by Directional Image Partitioning”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, No. 5, pp. 402-421, 1999.

  • Lukasz Wieclaw, “Gradient Based Fingerprint Orientation Field Estimation”, Journal of Medical Informatics and Technologies, Vol. 22, pp. 202-207, 2013.

  • Sharat Chikkerur, Alexander N. Cartwright and Venu Govindaraju, “Fingerprint Enhancement using STFT Analysis”, Pattern Recognition, Vol. 40, No. 1, pp. 198-211, 2007.

  • Yi Wang, Jiankun Hu and Fengling Han, “Enhanced Gradient-based Algorithm for the Estimation of Fingerprint Orientation Fields”, Applied Mathematics and Computation, Vol. 185, No. 1, pp. 823-833, 2007.

  • Asker M. Bazen and Sabih H. Gerez, “Directional Field Computation for Fingerprints Based on the Principal Component Analysis of Local Gradients”, Proceedings on Workshop on Circuits, Systems and Signal Processing, pp. 1-7, 2000.

  • M.A. Oliveira and N.J. Leite, “A Multiscale Directional Operator and Morphological Tools for Reconnecting Broken Ridges in Fingerprint Images”, Pattern Recognition, Vol. 41, No. 1, pp. 367-377, 2008.


Abstract Views: 229

PDF Views: 1




  • Reliable Orientation Field Estimation of Fingerprint Based on Adaptive Neighborhood Analysis

Abstract Views: 229  |  PDF Views: 1

Authors

Shoba Dyre
Department of Computer Science, Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women, India

Abstract


Fingerprint Orientation estimation is an important step in feature extraction and classification. However, a reliable extraction of fingerprint orientation data is still a challenge for poor quality images. In this paper, a gradient based estimation of orientation field based on the analysis of orientation consistency in the neighborhood for regularizing the orientation field is proposed. Experimental results are analyzed and compared with other existing gradient based methods used in this work. Evaluation performed on standard FVC2002 fingerprint databases DB1, DB2 and sample fingerprint images collected using optical fingerprint reader exhibit visibly better orientation estimation for various quality images using the proposed method.

Keywords


Gradient-Based Method, Orientation Map, Gaussian Filter, Orientation Smoothing, Orientation Consistency.

References