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Computational Intelligence in Data Mining—Volume 1 pp 29–37Cite as

Pose Invariant Face Recognition for New Born: Machine Learning Approach

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 410))

Abstract

Pose is a natural and important covariate in case of newborn and face recognition across pose can troubleshoot the approaches dealing with uncooperative subjects like newborn, in which the full power of face recognition being a passive biometric technique requires to be implemented and utilized. To handle the large pose variation in newborn, we propose a pose-adaptive similarity method that uses pose-specific classifiers to deal with different combinatorial poses. A texture based face recognition method, Speed Up Robust Feature (SURF) transform, is used to compare the descriptor of testing (probe) face with given training (gallery) face descriptor. Probes executed on the face template data of newborn described here, offer comparative benefits towards affinity for pose variations and the proposed algorithm verdicts the rank 1 accuracy of 92.1 %, which demonstrates the strength of self learning even with single training face image of newborn.

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References

  1. Jia, W., Cai, H., Gui, J., et al.: Newborn footprint recognition using orientation feature. J. Neural Comput. Appl. 21(8), 1855–1863 (2012)

    Article  Google Scholar 

  2. Gray, J.E., Suresh, G., Ursprung, R., Edwards, W.H., Nickerson, J., Shinno, P.H.: Patient misidentification in the neonatal intensive careunit: quantification of risk. Pediatrics 117, e46–e47 (2006)

    Article  Google Scholar 

  3. http://www.amfor.net/stolenbabies.html. Accessed 25 May 2011

  4. Stapleton, M.E.: Best foot forward: newborn footprints for personalidentification. FBI Law Enforcement Bull. 63(11), 1999

    Google Scholar 

  5. Thompson, J.E., Clark, D.A., Salisbury, B., Cahill, J.: Footprinting the: not cost-effective. J. Pediatr. 99, 797–798 (1981)

    Article  Google Scholar 

  6. Galton, F.: Finger prints of young children (British Association for the Advancement of Science, 1899)

    Google Scholar 

  7. Shepard, K.S., Erickson, T., Fromm, H.: Limitations of footprinting as a means of newborn identification. Pediatrics 37(1), 107–108 (1966)

    Google Scholar 

  8. Pela, N.T.R., Mamede, M.V., Tavares, M.S.G.: Analise crtica de impressoes plantares de recem-nascidos. Revista Brasileira de Enfermagem 29, 100–105 (1975)

    Article  Google Scholar 

  9. Weingaertner, D., Bellon, O.R.P., Cat, M.N.L., Silva, L.: Newborn’s biometric identification: can it be done? Int. Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2008

    Google Scholar 

  10. Fields, C., Hugh, C.F., Warren, C.P., Zimberoff, M.: The ear of the newborn as an identification constant. J. Obstet. Gynecol. 16, 98–101 (1960)

    Google Scholar 

  11. Lemes, R.P., Bellon, O.R.P., Silva, L., Jain, A.K.: Biometric recognition of newborns: identification using palmprints, pp. 11–13. Int. Joint Conf. Biometrics, Washington, DC, USA (2011)

    Google Scholar 

  12. Tiwari, S., Singh, S.K.: Face recognition for newborns. IET Biometrics. 1(4), 200–208 (2012)

    Article  Google Scholar 

  13. Tiwari, S., Singh, S.K.: Newborn verification using headprint. J. Inform. Technol. Res. (JITR), 5(2), 15–30, April–June 2012, ISSN: 1938-7857, doi:10.4018/jitr.2012040102. (IGI Global)

    Google Scholar 

  14. Tiwari, S., Singh, A., Singh, S.K.: Fusion of ear and soft-biometrics for recognition of newborn. Sign. Image Process.: Int. J. 3(3), 103–116, ISSN: 0976-710X (2012)

    Google Scholar 

  15. Tiwari, S., Singh, Aruni, Singh, S.K.: Intelligent method for face recognition of infant. Int. J. Comput. Appl. 52(4), 36–50 (2012)

    Google Scholar 

  16. Tiwari, Shrikant, Singh, Aruni, Singh, Sanjay Kumar: Integrating Faces and Soft-biometrics for Newborn Recognition. Int. J. Adv. Comput. Eng. Archit. 2(2), 201–209 (2012)

    MathSciNet  Google Scholar 

  17. Tiwari, S., Singh, A., Singh, S.K.: Can Face and Soft-biometric Traits Assist in Recognition of Newborn? In: Proceedings of IEEE, International Conference on Recent Advanced in Information Technology, pp. 74–79, March 2012

    Google Scholar 

  18. Tiwari, S., Singh, A., Singh, S.K.: Can Ear and Soft-biometric Traits Assist in Recognition of Newborn? In: Proceedings of International Conference on Computer Science, Engineering and Applications, doi:10.1007/978-3-642-30157-5, ISBN: 978-3-642-30157-5, pp. 179–192. Springer, Berlin (May 2012)

    Google Scholar 

  19. Tiwari, S., Singh, S.K., Multimodal Biometric Recognition for Newborn. In: Srivastava, R., Singh, S.K., Shukla, K.K. (eds.) Research Developments in Biometrics and Video Processing Techniques (IGI Global Publishing). Chapter 2. ISSN: 1948-9730, ISBN: 978-1-4666-4870-8

    Google Scholar 

  20. Tiwari, S., Singh, A., Singh, S.K.: Multimodal database of newborns for biometric recognition. Int. J. Bio-Sci. Bio-Technol. 5(2), 89–99 (2013)

    Google Scholar 

  21. Tiwari, S., Singh, A., Singh S.K.: Newborn’s ear recognition: can it be done?. In: International Conference on Image Information Processing (ICIIP), JUIT India, 3–5 November 2011

    Google Scholar 

  22. Keogh, E., Wei, L.: Semi-supervised time series classification. In: SIGKKD (2006)

    Google Scholar 

  23. Chapelle, O., Scholkopf, B., Zien, A.: Semi-Supervised Learning. The MIT Press, Cambridge (2006)

    Book  Google Scholar 

  24. Dillon, J.V., Balasubramanian, K., Lebanon, G.: Asymptotic analysis of generative semi-supervised learning. In: Appearing in Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel (2010)

    Google Scholar 

  25. Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Surf: speeded up robust features. Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

  26. Giraldo, J., Vivas, N.M., Vila, E., Badia, A.: Assessing the (a) symmetry of concentration-effect curves: empirical versus mechanistic models. Pharmacol. Ther. 95(1), 21–45 (2002)

    Article  Google Scholar 

  27. Richards, F.J.: A flexible growth function for empirical use. J. Exp. Bot. 10, 290–300 (1959)

    Article  Google Scholar 

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Acknowledgments

The authors would like to thank Dr. Shrikant Tiwari (Department of Computer Science and Engineering, IIT (BHU) for providing database of newborn.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Indian School of Mines, Dhanbad, Jharkhand, India

    Rishav Singh & Hari Om

Authors
  1. Rishav Singh
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  2. Hari Om
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Corresponding author

Correspondence to Rishav Singh .

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Editors and Affiliations

  1. Veer Surendra Sai University of Tech., Dept of Computer Sci Eng & IT, Sambalpur, Odisha, India

    Himansu Sekhar Behera

  2. National Institute of Technology, Dept. of Computer Science & Engineering, Rourkela, Odisha, India

    Durga Prasad Mohapatra

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Singh, R., Om, H. (2016). Pose Invariant Face Recognition for New Born: Machine Learning Approach. In: Behera, H., Mohapatra, D. (eds) Computational Intelligence in Data Mining—Volume 1. Advances in Intelligent Systems and Computing, vol 410. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2734-2_4

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  • DOI: https://doi.org/10.1007/978-81-322-2734-2_4

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2732-8

  • Online ISBN: 978-81-322-2734-2

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