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A Hidden Markov Model (HMM) Scheme for Lip based Identification Utilizing Vertical Grooves Angles

by Alireza Shafii Mousavi, Houman Zarrabi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 29
Year of Publication: 2018
Authors: Alireza Shafii Mousavi, Houman Zarrabi
10.5120/ijca2018916678

Alireza Shafii Mousavi, Houman Zarrabi . A Hidden Markov Model (HMM) Scheme for Lip based Identification Utilizing Vertical Grooves Angles. International Journal of Computer Applications. 179, 29 ( Mar 2018), 45-50. DOI=10.5120/ijca2018916678

@article{ 10.5120/ijca2018916678,
author = { Alireza Shafii Mousavi, Houman Zarrabi },
title = { A Hidden Markov Model (HMM) Scheme for Lip based Identification Utilizing Vertical Grooves Angles },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2018 },
volume = { 179 },
number = { 29 },
month = { Mar },
year = { 2018 },
issn = { 0975-8887 },
pages = { 45-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number29/29165-2018916678/ },
doi = { 10.5120/ijca2018916678 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:56:56.087135+05:30
%A Alireza Shafii Mousavi
%A Houman Zarrabi
%T A Hidden Markov Model (HMM) Scheme for Lip based Identification Utilizing Vertical Grooves Angles
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 29
%P 45-50
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

As the lips move too quickly, the shape of lips images may differ from one situation to another for an individual such as the cases of smiling or frowning. In this paper, we propose a novel approach to automatic classification of lips images which are captured by digital camera. We focus on the lower part of lips based on morphological features of the grooves. Image processing techniques are employed to replace the traditional lip-prints to detect human identification with intelligent systems. Presented methodology takes lower part of the lips as an input to process their orientation of grooves. Morphological and structural features are considered to describe the lips. A Hidden Markov Model is proposed for classification and training. Results show the proposed algorithm is robust against scale changes, noises and multiple orientations of lip images for each person. With this approach 93.4%, 88.3% and 85.1% accuracy is achieved in classification of 5, 10, 15 class respectively on 75 lip samples. Presented algorithm may contribute significantly for the development of applications related to human identifications or can be used as a supplement to lip identification systems (for example, methods which are using contour of the lips) to reduce the error rate of classification.

References
  1. S.-L. Wang and A. W.-C. Liew, “Physiological and behavioral lip biometrics: A comprehensive study of their discriminative power,” Pattern Recognition, vol. 45, no. 9, pp. 3328 – 3335, 2012.
  2. H. F. E.-O. M. Wael M Saad, Assem H Kamel, “Genetic studies on the inheritance of lip prints in-cleft lip and palate,” Egypt J Plast Reconstr Surg, pp. 9–12, 2009.
  3. B. Karim and D. Gupta, “Cheiloscopy and blood groups: Aid in forensic identification,” The Saudi Dental Journal, vol. 26, no. 4, pp. 176 – 180, 2014.
  4. T. Y. Suzuki K, “Two criminal cases of lip print,”. ACTA Criminol, pp. Jpn 41: 61–64, 1975.
  5. Y. Tsuchihashi, “Studies on personal identification by means of lip prints,” Forensic Science, vol. 3, no. 0, pp. 233 – 248, 1974.
  6. K. RK, “Lip prints an identification aid,” Kathmandu Univ Med Je, vol. 38, no. 2, pp. 55–7, 2012.
  7. T. J. Augustine J, Barpande SR, “Cheiloscopy as an adjunct to forensic identification: A study of 600 individuals,” J Forensic Odontostomatole, vol. 26, pp. 44–52, 2008.
  8. V. A. T. S. P. S. Bajracharya D, Mainali A, “Cheiloscopy: An aid in gender identification,” J Nepal Dent Assoc, vol. 2, pp. 80–83, 2013.
  9. R. K. Saraswathi TR, Gauri Mishra, “Study of lip prints,” Journal of Forensic Dent Science, pp. 28–31, 2009.
  10. S. Bhattacharjee, S. Arunkumar, and S. K. Bandyopadhyay, “Article: Personal identification from lip-print features using a statistical model,” International Journal of Computer Applications, vol. 55, no. 13, pp. 30–34, October 2012.
  11. L. Smacki and K. Wrobel, “Lip print recognition based on mean differences similarity measure,” in Computer Recognition Systems 4, ser. Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2011, vol. 95, pp. 41–49.
  12. M. Chora, “The lip as a biometric,” Pattern Analysis and Applications, vol. 13, no. 1, pp. 105–112, 2010.
  13. K. Wrobel, R. Doroz, and M. Palys, “A method of lip print recognition based on sections comparison,” in Biometrics and Kansei Engineering (ICBAKE), 2013 International Conference on, July 2013, pp. 47–52.
  14. P. Porwik and T. Orczyk, “Dtw and voting-based lip print recognition system,” in Computer Information Systems and Industrial Management, 2012, vol. 7564, pp. 191–202.
  15. S. Bakshi, R. Raman, and P. Sa, “Lip pattern recognition based on local feature extraction,” in India Conference (INDICON), 2011 Annual IEEE, Dec 2011, pp. 1–4.
  16. A. Mehra, M. Kumawat, R. Ranjan, B. Pandey, S. Ranjan, A. Shukla, and R. Tiwari, “Expert system for speaker identification using lip features with pca,” in Intelligent Systems and Applications (ISA), 2010 2nd International Workshop on, May 2010, pp. 1–4.
  17. W. Niblack, An Introduction to Digital Image Processing. Strandberg Publishing Company, 1985.
  18. S. L. L. Lam and C. Suen, “Thinning methodologies -a comprehensive survey,” IEEE Transactions on Pattern Analysis and Machine Intelli-gence, vol. 14, pp. 869–885, 1992.
  19. R. Shakoori, “A method for text-line segmentation for unconstrained arabic and persian handwritten text image,” in Information Reuse and Integration (IRI), 2014 IEEE 15th International Conference on, Aug 2014, pp. 38–344.
  20. J. Illingworth and J. Kittler, “A survey of the hough transform,” Comput. Vision Graph. Image Process., vol. 44, no. 1, pp. 87–116, Aug. 1988.
  21. M. T. Parvez and S. A. Mahmoud, “Offline arabic handwritten text recognition: A survey,” ACM Comput. Surv., vol. 45, no. 2, pp. 23:1– 23:35, Mar. 2013.
  22. M. Khorsheed, “Off-line arabic character recognition - a review,” Pattern Analysis and Applications, vol. 5, no. 2, pp. 31–45, 2002.
  23. L. Rabiner and B. Juang, “An introduction to hidden markov models,” ASSP Magazine, IEEE, vol. 3, no. 1, pp. 4–16, Jan 1986.
  24. L. Rabiner, “A tutorial on hidden markov models and selected applica-tions in speech recognition,” Proceedings of the IEEE, vol. 77, no. 2, pp. 257–286, Feb 1989.
  25. A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, “From few to many: Illumination cone models for face recognition under variable lighting and pose,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 6, pp. 643–660, 2001.
  26. F. Solina, P. Peer, B. Batagelj, S. Juvan, and J. Kovac, “Color-based face detection in the ”15 seconds of fame” art installation,” in in Proceedings of Mirage 2003, (INRIA Rocquencourt, 2003, pp. 38–47.
Index Terms

Computer Science
Information Sciences

Keywords

Pattern recognition Image processing

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