Finger knuckle biometrics for personal identification using statistical and feature based approaches

  • Authors

    • Shubhangi Neware Shri Ramdeobaba College of Engineering and Management
    2019-03-22
    https://doi.org/10.14419/ijet.v7i4.17193
  • Finger Knuckle Print (FKP), Principal Component Analysis (PCA), Radon Like Features (RLF), EER (Equal Error Rate), DI (Decidability Index), CRR (Correct Recognition Rate).
  • Texture pattern observed on finger knuckle joint is called as Finger Knuckle Print (FKP). FKP can be extracted from inner side or back side of finger surface. FKP is extremely unique and makes the knuckle surface an emerging biometric modality. The FKP may be useful in user identification and has attracted attention of very few researchers. Our proposed work is based on FKP of back side of finger surface. The objective of this work is to investigate and develop a systematic approach for identifying a person using his/her finger knuckle print. Three different methodologies are employed for FKP identification process. Statistical approach (Principal Component Analysis) and feature based approaches (Gabor based coding scheme and Radon like features) are used to accomplish this objective. Nearest Mean Classification, Angular distance matching and Dynamic Time Warping methods are used for finding similarity between enrolled FKP and test FKP images. Performance comparison of proposed three methodologies is done by computing different performance measures such as FAR, FRR, EER, Di and CRR. Proposed methods are producing EER 0.2 using statistical approach and 0.03 using feature based approach.

     

     
  • References

    1. [1] A. Kumar, C. Ravikanth (2009), Personal Authentication Using Finger Knuckle Surface, IEEE Transactions on Information Forensics and Security, Vol 4, no 1, 98 –110.

      [2] A. Kong (2008), an Evaluation of Gabor Orientation as a Feature for Face Recognition, Proceedings of International Conference on Pattern Recognition, IEEE, 1-4.

      [3] A. K. Jain, P.J. Flynn, A. Ross (2007), Handbook of Biometrics, Springer-Verlag,USA.

      [4] A. Kumar, C. Ravikanth (2007), Biometric Authentication Using Finger Back Surface, Proceedings of IEEE Intl. Conference on Computer Vision and Pattern Recognition, 1-6.

      [5] A. Kumar, Y. Zhou (2009), Personal Identification using Finger Knuckle Orientation Features, Electronics Letters,Vol. 45, no. 20.

      [6] L. Zhang, D. Zhang (2009), Finger-Knuckle Print: A New Biometric Identifier, Image Processing ICIP, IEEE International Conference, 1981-84.

      [7] L. Zhang, Zhang L, D. Zhang, H. Zhu (2010), Online Finger-Knuckle-Print Verification for Personal Authentication, Pattern Recognition, Elsevier. Vol. 43, no.7, 2560–2571

      [8] L. Zhang, Zhang L, Zhang D, Zhu H (2011), Ensemble of Local and Global Information for Finger-Knuckle-Print Recognition, Pattern Recognition, Elsevier, Vol. 44, no. 9, 1990 – 1998.

      [9] L. Zhang (2011), Personal Authentication using Finger knuckle print, Ph.D Thesis, Hong Kong Polytechnic University.

      [10] G. S. Badrinath, A. Nigam, P. Gupta (2011), An Efficient Finger-knuckle-print based Recognition System Fusing SIFT and SURF Matching Scores, Information and Communication Security, ISBN 978-3-642-25242-6,374-387.

      [11] Hongyang Yu, Gongping Yang, Zhuoyi Wang and Lin Zhang, A New Finger-Knuckle-Print ROI Extraction Method Based on Two-Stage Center Point Detection, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 8, No. 2 (2015), pp. 185-200 .

      [12] K. Mahesh, K. Premalatha (2014), Finger Knuckle Print Identification Based on Local and Global Feature Extraction using SDOST, American Journal of Applied Sciences, Vol. 11, no.6, ISSN:1546-9239, 929-938.

      [13] K. Mehta, S. Neware, A.S. Zadgaonkar (2014), Finger Knuckle Feature Extraction using Radon like Features, International Journal of Computer Science and Communication, ISSN 0973-7391, Vol 5, 134-137.

      [14] S. Neware, K. Mehta, A.S. Zadgaonkar (2012), Finger Knuckle Surface Biometrics, International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, ISO 9001:2008 Certified Journal, Vol. 2, no. 12.

      [15] S. Neware, K. Mehta, A.S. Zadgaonkar (2013), Finger Knuckle Identification using Principal Component Analysis and Nearest Mean Classifier, International Journal of Computer Applications (0975 – 8887) Vol. 70, no.9,

      [16] S. Neware, K. Mehta, A.S. Zadgaonkar (2014), Finger Knuckle Print Identification using Gabor Features, International Journal of Computer Applications (0975 – 8887) Vol. 98, no.16.

      [17] S. Neware, K. Mehta, A.S. Zadgaonkar (2015), Finger Knuckle Identification using RLF and Dynamic Time Warping, International Journal of Computer Applications (0975 – 8887) Vol 119, no.3.

  • Downloads

  • How to Cite

    Neware, S. (2019). Finger knuckle biometrics for personal identification using statistical and feature based approaches. International Journal of Engineering & Technology, 7(4), 5213-5217. https://doi.org/10.14419/ijet.v7i4.17193