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Applied Intelligence

, Volume 49, Issue 5, pp 1823–1840 | Cite as

Improving the performance of the lip identification through the use of shape correction

  • Carlos M. Travieso
  • Antonio G. Ravelo-GarcíaEmail author
  • Jesús B. Alonso
  • José M. Canino-Rodríguez
  • Malay Kishore Dutta
Article
  • 64 Downloads

Abstract

This work presents automatic identification and verification approaches based on lip biometrics, using a static lip shape and applying a lip correction preprocessing, transforming data from Hidden Markov Model and being classified by Support Vector Machines. The classification system is conclusive for the identification of a person by the shape of the lips, even if the person presents soft facial emotions. Moreover the use of static lips shape has been revealed as a good option for security applications. The experiments have been carried out with two public datasets. One dataset was used to model and validate the approach, and the other dataset has been used to test the model blindly. The accuracy is up to 100% and 99.76% for GDPS-ULPGC and RaFD datasets respectively, using two training samples under a hold-out validation. Based on the results we can conclude that the system is very robust and stable with the highest classification capacity and minimal computation complexity.

Keywords

Lip-based biometrics Image processing Pattern recognition Automatic identification Artificial intelligence 

Notes

Acknowledgements

This work has been suported by Ministerio de Economía y Competitividad (TEC2016-77791-C4-1-R).

References

  1. 1.
    Travieso CM, Zhang J, Miller P, Alonso JB, Ferrer MA (2011) Bimodal biometric verification based on face and lips. Neurocomputing 74(14):2407–2410CrossRefGoogle Scholar
  2. 2.
    Wang SL, Liew AWC (2012) Physiological and behavioral lip biometrics: A comprehensive study of their discriminative power. Pattern Recogn 45(9):3328–3335CrossRefGoogle Scholar
  3. 3.
    Mehra A, Kumawat M, Ranjan R, Pandey B, Ranjan S, Shukla A, Tiwari R (2010) Expert system for speaker identification using lip features with PCA. In Intelligent Systems and Applications (ISA), 2010 2nd International Workshop on (pp. 1–4)Google Scholar
  4. 4.
    Arsic I, Vilagut R, Thiran JP (2006) Automatic extraction of geometric lip features with application to multi-modal speaker identification. In Multimedia and Expo, 2006 IEEE International Conference on (pp. 161–164)Google Scholar
  5. 5.
    Newman JL, Cox SJ (2009). Automatic visual-only language identification: A preliminary study. In Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on (pp. 4345–4348). IEEEGoogle Scholar
  6. 6.
    Newman JL, Cox SJ (2010) Speaker independent visual-only language identification. In Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on (pp. 5026–5029). IEEEGoogle Scholar
  7. 7.
    Chan CH, Goswami B, Kittler J, Christmas W (2012) Local ordinal contrast pattern histograms for spatiotemporal, lip-based speaker authentication. IEEE Transactions on Information Forensics and Security 7(2):602–612CrossRefGoogle Scholar
  8. 8.
    Briceño JC, Travieso CM, Alonso JB, Ferrer MA (2010) Robust identification of persons by lips contour using shape transformation. In Intelligent Engineering Systems (INES), 2010 14th International Conference on (pp. 203–207). IEEEGoogle Scholar
  9. 9.
    Raheja JL, Shyam R, Gupta J, Kumar U, Prasad PB (2010) Facial gesture identification using lip contours. In Machine Learning and Computing (ICMLC), 2010 Second International Conference on (pp. 3–7). IEEEGoogle Scholar
  10. 10.
    Rohani R, Alizadeh S, Sobhanmanesh F, Boostani R (2008) Lip segmentation in color images. In Innovations in Information Technology, 2008. IIT 2008. International Conference on (pp. 747–750). IEEEGoogle Scholar
  11. 11.
    Shokhan MH, Khitam AM (2015) Biometric Identification System by Lip Shape. International Journal of Advanced Computer Research 5(18):19Google Scholar
  12. 12.
    Shen X, Wang Z, Wang Y, Chen H (2015) Dynamic Lip Identification Algorithm Based on Automatic Calibration of Feature Points. Journal of Information & Computational Science 12(15):5659–5665CrossRefGoogle Scholar
  13. 13.
    Kumaran M, Bastia BK, Kumar L, Patel SH (2017) Correlation between Fingerprint and Lip Print Pattern in Gujarati Population. Medico-Legal Update 17(1)Google Scholar
  14. 14.
    Sharma R, Sharma K, Preethi N, Degra H, Rajmani H (2017) Cheiloscopy: A Study of Morphological patterns of Lip Prints in Rajasthani population. Journal of Research in Medical and Dental Science 3(1):35–38CrossRefGoogle Scholar
  15. 15.
    Thakur B, Ghosh B, Puri N, Bansal R, Yadav S, Sharma RK (2017) A comparative study of lip print patterns in monozygotic and dizygotic twins. International Journal of Research in Medical Sciences 5(5):2144–2149CrossRefGoogle Scholar
  16. 16.
    Alzapur A, Nagothu RS, Nalluri HB (2017) Lip prints-A study of its uniqueness among students of MediCiti Medical College. Indian Journal of Clinical Anatomy and Physiology 4(1):68Google Scholar
  17. 17.
    Travieso CM, Zhang J, Miller P, Alonso JB (2014) Using a Discrete Hidden Markov Model Kernel for lip-based biometric identification. Image Vis Comput 32(12):1080–1089CrossRefGoogle Scholar
  18. 18.
    Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57(2):137–154CrossRefGoogle Scholar
  19. 19.
    Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 9(1):62–66CrossRefGoogle Scholar
  20. 20.
    Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, OxfordzbMATHGoogle Scholar
  21. 21.
    Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286CrossRefGoogle Scholar
  22. 22.
    Cervantes J, Li X, Yu W, Li K (2008) Support vector machine classification for large data sets via minimum enclosing ball clustering. Neurocomputing 71(4):611–619CrossRefGoogle Scholar
  23. 23.
    Hernando J, Nadeu C, Mariño J (1997) Speech recognition in a noisy car environment based on LP of the one-sided autocorrelation sequence and robust similarity measuring techniques. Speech Comm 21(1–2):17–31CrossRefGoogle Scholar
  24. 24.
    Rabiner L, Juang B (1986) An introduction to hidden Markov models. IEEE ASSP Mag 3(1):4–16CrossRefGoogle Scholar
  25. 25.
    Ferrer MA, Alonso JB, Travieso CM (2005) Offline geometric parameters for automatic signature verification using fixed-point arithmetic. IEEE Trans Pattern Anal Mach Intell 27(6):993–997CrossRefGoogle Scholar
  26. 26.
    David S, Ferrer MA, Travieso CM, Alonso JB (2004) GPDSHMM: A hidden Markov model toolbox in the MATLAB environment. CSIMTA, Complex Systems Intelligence and Modern Technological Applications, 476–479Google Scholar
  27. 27.
    Jaakkola T, Diekhans M, Haussler D (2000) A discriminative framework for detecting remote protein homologies. J Comput Biol 7(1–2):95–114CrossRefGoogle Scholar
  28. 28.
    Langner O, Dotsch R, Bijlstra G, Wigboldus DH, Hawk ST, Van Knippenberg AD (2010) Presentation and validation of the Radboud Faces Database. Cognit Emot 24(8):1377–1388CrossRefGoogle Scholar
  29. 29.
    Braga-Neto UM, Dougherty ER (2004) Is cross-validation valid for small-sample microarray classification? Bioinformatics 20(3):374–380CrossRefGoogle Scholar
  30. 30.
    Wrobel K, Doroz R, Porwik P, Naruniec J, Kowalsk M (2017) Using a probabilistic neural network for lip-based biometric verification. Eng Appl Artif Intell 64:112–127CrossRefGoogle Scholar
  31. 31.
    Wang S-L, Liew AW-C (2012) Physiological and behavioral lip biometrics: A comprehensive study of their discriminative power. Pattern Recogn 45:3328–3335CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute for Technological Development and Innovation in Communications, Signals and Communications Department, IDeTIC-ULPGCUniversity of Las Palmas de Gran CanariaLas PalmasSpain
  2. 2.Signals and Communications DepartmentUniversity of Las Palmas de Gran CanariaLas PalmasSpain
  3. 3.Centre for Advanced StudiesDr. A.P.J. Abdul Kalam Technical UniversityLucknowIndia

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