Biometric Inheritance Pattern Synthesis and Features’ Extraction

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Information and Communication Technology for Competitive Strategies (ICTCS 2022) (ICTCS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 623))

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Abstract

Inheritance is the process of passing hereditary attributes or genetic characteristic from one age group of entities to the following or their descendants and they get some characteristic from their parents. Biometric inheritance attributes clarify the characteristic of the parents and child that they have common characteristic traits. These characteristic traits are passed by parents to child as eye color, hair color, hair texture, lips minutiae, blood type, and many more. The fingerprint authentication of a person is most popular manner due to fast response and easy to handle, and it is widely used in different places to check user authenticity compare to other authentication manner. As finger and its print are part of human body, so there is a logic that some part of it should match with the parent fingerprint. This research is aimed to synthesis of biometric inheritance patterns (fingerprint and palmprint) and its features extraction or recognition method. The finding of similarity between parent and children biometric pattern (fingerprint and palmprint) is the objective of the research work. Here, the research covers fingerprint and palmprint patterns, recognition method like minutiae matching, and its implementation on machine learning tools like MATLAB.

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References

  1. Dastbaz M, Halpin E, Wright S (2013) Book-strategic intelligence management chapter 10—emerging technologies and the human rights challenge of rapidly expanding state surveillance capacitie. Science Direct

    Google Scholar 

  2. Economic times (2019) The future of security. so far, biometrics’ the way to go, ET Bureau, Dec

    Google Scholar 

  3. Mathuria M (2015) Fingerprint recognition based on minutiae information. IJCA

    Google Scholar 

  4. Mathuria M (2013) Fingerprint classification based on orientation estimation, IJARCS

    Google Scholar 

  5. Mathuria M (2018) Mobile banking transaction using fingerprint authentication. IEEE

    Google Scholar 

  6. Khodadoust J (2017) Fingerprint indexing based on minutiae pairs and convex core point. Pattern Recognit 67:110–126. ISSN 0031-3203. https://doi.org/10.1016/j.patcog.2017.01.022

  7. Fish JT, Miller LS, Braswell MC (2014) Crime scene investigation. 3rd (ed) Elsevier

    Google Scholar 

  8. MedlinePlus Homepage. https://medlineplus.gov/genetics/understanding/traits/fingerprints

  9. Science Buddies Homepage (2020). https://www.sciencebuddies.org/science-fair-projects/project-ideas/Genom_p009/genetics-genomics/are-fingerprint-patterns-inherited

  10. Aigbogun EO (2019) Fingerprint pattern similarity: a family-based study using novel classification. Int J Exp Clin Anat. https://doi.org/10.2399/ana.19.065

  11. Adam EEB, Ammayappan S (2021) Evaluation of fingerprint liveness detection by machine learning approach—a systematic view. J ISMAC IoT Soc Mob Anal Cloud 3(1). ISSN: 2582-1369. https://doi.org/10.36548/jismac.2021.1.002

  12. Chinnappan C (2021) Fingerprint recognition technology using deep learning: a review. Int J Creat Res Thoughts (IJCRT) 9. ISSN: 2320-2882

    Google Scholar 

  13. Naveen M (2021) Machine learning algorithms based palmprint biometric identification. Int J Eng Res Technol (IJERT) 9. ISSN: 2278-0181, ICCIDT

    Google Scholar 

  14. Yadav JKPS (2020) A short review on machine learning techniques used for fingerprint recognition. J Crit Rev 7. ISSN: 2394-5125

    Google Scholar 

  15. Prasanna YSDL (2020) Palm print recognition using inner finger deep learning using neural network. Int J Adv Sci Res Eng Trends 5. ISSN (Online) 2456-0774

    Google Scholar 

  16. Tamrakar A, Gupta N (2019) A study on machine learning approach for fingerprint recognition system. Int J Bio Sci (IJO-SCIENCE) 5(11). ISSN: 2455-0108. https://doi.org/10.24113/ijoscience.v5i11.234

  17. Nguyen HT, Nguyen LT (2019) Fingerprints classification through image analysis and machine learning method. Algorithms. https://doi.org/10.3390/a12110241

  18. Kundu P (2019) A survey on fingerprint pattern recognition. Int J Res GRANTHAALAYAH. ISSN: 2350-0530(O), ISSN: 2394-3629(P). https://doi.org/10.29121/granthaalayah.v7.i8.2019.704

  19. Mathuria M (2012) Fingerprint matching using ridge-end and bifurcation points. IJCA

    Google Scholar 

  20. Mathuria M (2020) Improved fingerprint recognition using filtering techniques. Springer

    Google Scholar 

  21. Mathuria M (2013) Fingerprint minutiae matching using region of interest. IJCTT

    Google Scholar 

  22. Mathuria M (2016) Quality improvement of fingerprint recognition system. Springer

    Google Scholar 

  23. Mathuria M (2019) User identification over digital social network using fingerprint authentication. Springer

    Google Scholar 

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Correspondence to Vimlesh Sharma .

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Sharma, V., Gupta, Y.K. (2023). Biometric Inheritance Pattern Synthesis and Features’ Extraction. In: Joshi, A., Mahmud, M., Ragel, R.G. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2022). ICTCS 2022. Lecture Notes in Networks and Systems, vol 623. Springer, Singapore. https://doi.org/10.1007/978-981-19-9638-2_29

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  • DOI: https://doi.org/10.1007/978-981-19-9638-2_29

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-9637-5

  • Online ISBN: 978-981-19-9638-2

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