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Information and Communication Technology for Intelligent Systems pp 675–684Cite as

A Novel Approach to the ROI Extraction in Palmprint Classification

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 106))

Abstract

Biometric Person Identification (BPI) plays important role in the security for the purposes of authentication, as pins and password are never reliable for certification. Recently in the biometric systems, touchless palmprint recognition system has focused on flexibility, more personal hygiene, and less time consumption. However, identification using touchless or pegless images also faces several severe challenges to find palm areas such as variations in rotation, shift/size, and complex background. In this paper, a robust rotation invariant, size/scale invariant preprocessing method for touchless palmprint has been proposed. This method has been implemented on standard databases of CASIA and IITD, where images are captured using the pegless/touchless scenario with a lot of variations in the rotation as well as the size of the palm.

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References

  1. Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol. 14(1), 4–20 (2004). https://doi.org/10.1109/tcsvt.2003.818349

  2. Zhang, D.: Palmprint Authentication. Kluwer Academic Publishers, USA (2004)

    Google Scholar 

  3. Han, Y., Sun, Z., Wang, F., Tan, T.: Palmprint recognition under unconstrained scenes. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) Computer Vision—ACCV 2007. Lecture Notes in Computer Science, vol. 4844. Springer, Berlin, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76390-1_1

  4. Feng, Y., Li, J., Huang, L., Liu, C.: Real-time ROI acquisition for unsupervised and touch-less palmprint. World Acad. Sci. Eng. Technol. Int. J. Comput. Inf. Eng. 5(6) (2011). https://doi.org/10.1999/1307-6892/8883

  5. Ito, K., Aoki, T.: Recent advances in biometric recognition. Inst. Image Inf. Telev. Eng. 6(1), 64–80 (2018). https://doi.org/10.3169/mta.6.64

    Article  Google Scholar 

  6. Mokni, R., Kherallah, M.: Lecture Notes in Computer Science, vol. 9887, p. 259 (2016). ISSN: 0302-9743, ISBN: 978-3-319-44780

    Google Scholar 

  7. Li, H., Guo, Z., Ma, S., Luo, N.: A new touchless palmprint location method based on contour centroid. In: 2011 International Conference on Hand-Based Biometrics Bandung, Indonesia (2011). https://doi.org/10.1109/ichb.2011.6094306

  8. Tamrakar, D., Khanna, P.: Analysis of palmprint verification using wavelet filter and competitive code. In: 2010 International Conference on Computational Intelligence and Communication Systems (2010). https://doi.org/10.13140/rg.2.1.4393.1124

  9. IITD Touchless palmprint Database (v1). http://www.comp.polyu.edu.hk/~csajaykr/IITD/Database_Palm.htm

  10. CASIA Palmprint Database. http://www.idealtest.org/dbDetailForUser.do?id=5

  11. Al-Kofahi, Y., Lassoued, W., Lee, W., Roysam, B.: Improved automatic detection and segmentation of cell nuclei in histopathology images. IEEE Senior Member 841–852 (2010) https://doi.org/10.1109/tbme.2009.2035102

  12. Vaidehi, K., Subashini, T.S.: Transform based approaches for palmprint identification. Int. J. Comput. Appl. (0975 8887) 41(1), 1 (2012). https://doi.org/10.5120/5502-7496

  13. Mokni, R., Kherallah, M.: Novel palmprint biometric system combining several fractal methods for texture information extraction, Systems Man and Cybernetics (SMC) 2016 IEEE International Conference on, pp. 002 267–02 272. (2016). https://doi.org/10.1109/SMC.2016.7844576

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Author information

Authors and Affiliations

  1. Department of Electronics and Telecommunication, Maharashtra Institute of Technology, Aurangabad, India

    Swati R. Zambre & Abhilasha Mishra

Authors
  1. Swati R. Zambre
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  2. Abhilasha Mishra
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Corresponding author

Correspondence to Swati R. Zambre .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India

    Suresh Chandra Satapathy

  2. Sabar Institute of Technology, Gujarat Technological University, Ahmedabad, Gujarat, India

    Amit Joshi

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© 2019 Springer Nature Singapore Pte Ltd.

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Zambre, S.R., Mishra, A. (2019). A Novel Approach to the ROI Extraction in Palmprint Classification. In: Satapathy, S., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems . Smart Innovation, Systems and Technologies, vol 106. Springer, Singapore. https://doi.org/10.1007/978-981-13-1742-2_67

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