A Cheiloscopic Approach for Unique Identification Among Indian Subpopulation

  • Shilpi Jain
  • V. Poojitha
  • Madhulika Bhatia
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 624)


A biometric is formed on an individual’s behavioural or physical features. The main approach is to uniquely identify humans. Identification by biometric factors finishes the complications related with customary approaches used for human identification. The biometric methods that are most commonly being used today are fingerprints, eye retina, iris, etc. This paper shows that just like fingerprints and lip prints are unique in nature and hence can be used as one of the measures to recognize individuals. Also, this paper shows that the nature of lips of an individual varies according to state one belongs to. The lip print samples are taken from different people in different states. After the enhancement of image, existing Sobel edge detection algorithm has been applied to detect the edges of lips. Thereafter, the numValue, i.e. featureValue of the lip print, is extracted and stored which depicts the uniqueness. The graphs have been plotted and examined.


Cheiloscopy Lip print Feature extraction Biometric identification Lip prints relation regionwise 


  1. 1.
    Kasprzak J, Leczynska B (2001) Chieloscopy. “Human identification on the basis of lip Prints” (in Polish). CLK KGP Press, Warsaw, 2001.Google Scholar
  2. 2.
    Shokhan, M. H., and A. M. Khitam. “Biometric Identification System by Lip Shape.” International Journal of Advanced Computer Research 5.18 (2015): 19.Google Scholar
  3. 3.
    Bhattacharjee, Saptarshi, S. Arunkumar, and Samir Kumar Bandyopadhyay. “Personal identification from lip-print features using a statistical model.” arXiv preprint arXiv:1310.0036 (2013).
  4. 4.
    Bandyopadhyay, Samir Kumar, S. Arunkumar, and Saptarshi Bhattacharjee. “Feature Extraction of Human Lip Prints.” arXiv preprint arXiv:1312.0852 (2013).
  5. 5.
    Poojitha, V., et al. “A collocation of IRIS flower using neural network clustering tool in MATLAB.” Cloud System and Big Data Engineering (Confluence), 2016 6th International Conference. IEEE, 2016.Google Scholar
  6. 6.
    Deepika, C. Lakshmi, and A. Kandaswamy. “An algorithm for improved accuracy in unimodal biometric systems through fusion of multiple feature sets.” ICGST-GVIP Journal, ISSN (2009): 33–40.Google Scholar
  7. 7.
    Ives, Robert W., et al. “A multidisciplinary approach to biometrics.” IEEE Transactions on Education 48.3 (2005): 462–471.Google Scholar
  8. 8.
    Sandhu, Parvinder S., et al. “Biometric methods and implementation of algorithms.” International Journal of Electrical and Electronics Engineering 3.8 (2009): 492–497.Google Scholar
  9. 9.
    Bhatia, Madhulika, et al. “Implementing edge detection for medical diagnosis of a bone in Matlab.” Computational Intelligence and Communication Networks (CICN), 2013 5th International Conference on. IEEE, 2013.Google Scholar
  10. 10.
    Bhatia, Madhulika, et al. “Proposed algorithm to blotch grey matter from tumored and non tumored brain MRI images.” Indian Journal of science and Technology 8.17 (2015).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Computer Science and EngineeringAmity UniversityNoidaIndia

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