Minutiae from Bit-Plane Sliced Thermal Images for Human Face Recognition

  • Ayan Seal
  • Debotosh Bhattacharjee
  • Mita Nasipuri
  • Dipak Kumar Basu
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 131)


In this paper an efficient approach for human face recognition based on the use of minutiae points is proposed. The thermogram of human face is captured by thermal infra-red camera. Image processing technologies are used to pre-process the captured thermogram. Then different physiological features are extracted using bit-plane slicing from the captured thermogram. These extracted features are called blood perfusion data. Blood perfusion data are characterized by the regional blood flow in human tissue and therefore do not depend entirely on surrounding temperature. These data bear a great potential for deriving discriminating facial thermogram for better classification and recognition of face images in comparison to static image data. Blood perfusion data are related to distribution of blood vessels under the face skin. Distribution of blood vessels is unique for each person and as set of extracted minutiae points from a blood perfusion data of a human face should be unique for that face. There may be several such minutiae point sets for a single face but all of these correspond to that particular face only. Entire face image is partitioned into equal consequence blocks and the total number of minutiae points from each block is computed to construct final vector. Therefore, the size of the feature vectors is found to be same as total number of blocks considered. A five layer feed-forward back propagation neural network is used as the classification tool. A number of experiments were conducted to evaluate the performance of the proposed face recognition system with varying block size. Experiments have been performed on the database created at our own laboratory. The maximum success of 95.24% recognition has been achieved with block size 8×8 and 32×32 with bit-plane 4 and accuracy rate of 97.62% has been achieved with block size 16×16 for bit-plane 4.


Thermal physiological feature bit-plane slicing minutiae point false acceptance rate false rejection rate 


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  1. Toth, B.: Biometric Liveness Detection. Information Security Bulletin (October 2005)Google Scholar
  2. Jain, A.K., Flynn, P., Ross, A.A.: Handbook of Biometrics. Springer (2007)Google Scholar
  3. Socolinsky, D., Selinger, A.: Face recognition with visible and thermal infrared imagery (2003)Google Scholar
  4. Wu, S., Fang, Z.-J., Xie, Z.-H., Liang, W.: Blood Perfusion Models for Infrared Face Recognition. School of information technology, Jiangxi University of Finance and Economics, ChinaGoogle Scholar
  5. Prokoski, F.: History, current status, and future of infrared identification. In: Proceedings of the IEEE Workshop Computer Vision Beyond Visible Spectrum: Methods and Applications, pp. 5–14 (2000)Google Scholar
  6. Kong, S.G., Heo, J., Abidi, B.R., Paik, J., Abidi, M.A.: Recent advances in visual and infrared face recognition: a review. Comput. Vision Image Understanding 97, 103–135 (2005)CrossRefGoogle Scholar
  7. Chen, X., Flynn, P.J., Bowyer, K.W.: IR and Visible light face Recognition. University of NotreDame, USA (2005)CrossRefGoogle Scholar
  8. Wu, S.Q., Gu, Z.H., Chia, K.A., Ong, S.H.: Infrared facial recognition using modified blood perfusion. In: Proceedings 6th Int. Conf. Inform., Comm. & Sign. Proc., Singapore, pp. 1–5 (December 2007)Google Scholar
  9. Heo, J., Savvides, M., Vijayakumar, B.V.K.: Performance Evaluation of Face Recognition using Visual and Thermal Imagery with Advanced Correlation Filters. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 (2005)Google Scholar
  10. Jones, B.F., Plassmann, P.: Digital infrared thermal imaging of human skin. IEEE Engineering in Medicine & Biology Magazine 21(6), 41–48 (2002)CrossRefGoogle Scholar
  11. Manohar, C.: Extraction of Superficial Vasculature in Thermal Imaging. Master’S Thesis, Dept. Electrical Eng., Univ. of Houston, Houston, Texas (December 2004)Google Scholar
  12. Guyton, A.C., Hall, J.E.: Textbook of Medical Physiology, 9th edn. W.B. Saunders Company, Philadelphia (1996)Google Scholar
  13. De Geef, S., Claes, P., Vandermeulen, D., Mollemans, W., Willems, P.G.: Large-Scale In-Vivo Caucasian Facial Soft Tissue Thickness Database for Craniofacial Reconstruction. Forensic Science 159(1), S126–S146 (2006)Google Scholar
  14. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall (2002)Google Scholar
  15. Galton, F.: Finger Prints. Mcmillan, London (1892)Google Scholar
  16. Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition, 2nd edn. Springer-Verlag London Limited (2009)CrossRefGoogle Scholar
  17. Jain, A., Ross, A., Prabhakar, S.: Fingerprint matching using minutiae and texture features. In: Proc. of Intl. Conf. on Image Processing, ICIP, Thessaloniki, Greece, October 7-10, pp. 282–285 (2001)Google Scholar
  18. Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1) (1991)CrossRefGoogle Scholar
  19. Turk, M., Pentland, A.: Face recognition using eigenfaces. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 586–591 (1991)Google Scholar
  20. Lin, Lee: Neural Fuzzy Systems. Prentice Hall International (1996)Google Scholar

Copyright information

© Springer India Pvt. Ltd. 2012

Authors and Affiliations

  • Ayan Seal
    • 1
  • Debotosh Bhattacharjee
    • 1
  • Mita Nasipuri
    • 1
  • Dipak Kumar Basu
    • 1
  1. 1.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia

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