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Histogram of Bunched Intensity Values Based Thermal Face Recognition

  • Ayan Seal
  • Debotosh Bhattacharjee
  • Mita Nasipuri
  • Consuelo Gonzalo-Martin
  • Ernestina Menasalvas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8537)

Abstract

A robust thermal face recognition method has been discussed in this work. A new feature extraction technique named as Histogram of Bunched Intensity Values (HBIVs) is proposed. A heterogeneous classifier ensemble is also presented here. This classifier consists of three different classifiers namely, a five layer feed-forward backpropagation neural network (ANN), Minimum Distance Classifier (MDC), and Linear Regression Classifier (LRC). A comparative study has been made based on other feature extraction techniques for image description. Such image description methods are Harris detector, Hessian matrix, Steer, Shape descriptor, and SIFT. In the classification stage ANN, MDC, and LRC are used separately to identify the class label of probe thermal face images. Another class label is also assigned by majority voting technique based on the three classifiers. The proposed method is validated on UGC-JU thermal face database. The matching using majority voting technique of HBIVs approach showed a recognition rate of 100% for frontal face images which, consists different facial expressions such as happy, angry, etc On the other hand, 96.05% recognition rate has been achieved for all other images like variations in pose, occlusion etc, including frontal face images. The highly accurate results obtained in the matching process clearly demonstrate the ability of the thermal infrared system to extend in application to other thermal-imaging based systems.

Keywords

Thermal face image Histogram of Bunched Intensity Values Minimum Distance Classifier Linear Regression Classifier Artificial Neural Network 

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References

  1. 1.
    Blanz, V., Vetter, T.: Face recognition based on tting a 3D morphable model. PAMI (2003)Google Scholar
  2. 2.
    Mohammed, U., Prince, S., Kautz, J.: Visio-lization: Generating novel facial images. ACM Trans. on Graphics (2009)Google Scholar
  3. 3.
    Nishiyama, M., Yamaguchi, O.: Face recognition using the classi ed appearance-based quotient image. AFG (2006)Google Scholar
  4. 4.
    Arandjelovic, O., Cipolla, R.: A pose-wise linear illumination manifold model for face recognition using video. CVIU (2009)Google Scholar
  5. 5.
    Wu, S., Fang, Z.-J., Xie, Z.-H., Liang, W.: Blood Perfusion Models for Infrared Face Recognition, pp. 183–207. School of information technology, Jiangxi University of Finance and Economics, China (2008)Google Scholar
  6. 6.
    Friedrich, G., Yeshurun, Y.: Seeing people in the dark: Face recognition in infrared images. In: BMVC (2003)Google Scholar
  7. 7.
    Pavlidis, I., Symosek, P.: The imaging issue in an automatic face/disguise detection system. In: CVBVS (2000)Google Scholar
  8. 8.
    Nicolo, F., Schmid, N.A.: A method for robust multispectral face recognition. In: ICIAR (2011)Google Scholar
  9. 9.
    Kong, S.G., Heo, J., Abidi, B.R., Paik, J., Abidi, M.A.: Recent advances in visual and in-frared face recognitiona review. Computer Vision Image Understanding 97, 103–135 (2005)CrossRefGoogle Scholar
  10. 10.
    Manohar, C.: Extraction of Super cial Vasculature in Thermal Imaging, masters thesis. Dept. Electrical Eng., Univ. of Houston, Houston, Texas (December 2004)Google Scholar
  11. 11.
    Harris, C., Stephens, M.: A Combined Corner and Edge Detector. In: Proc. Fourth Alvey Vision Conf., pp. 147–151 (1988)Google Scholar
  12. 12.
    Gradshteyn, I.S., Ryzhik, I.M.: Hessian Determinants, 14, 6th edn. 14.314 in Tables of Integrals, Series, and Products, p. 1069. Academic Press, San Diego (2000)Google Scholar
  13. 13.
    Freeman, W., Adelso, E.: The Design and Use of Steerable Filters. IEEE Trans. Pattern Analysis and Machine Intelligence 13(9), 891–906 (1991)CrossRefGoogle Scholar
  14. 14.
    Lowe, D.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  15. 15.
    Seal, A., Bhattacharjee, D., Nasipuri, M., Basu, D.K.: Minutiae based thermal face recognition using blood perfusion data. In: Processing, ICIIP (2011)Google Scholar
  16. 16.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice Hall (2002)Google Scholar
  17. 17.
    Naseem, R.T., Bennamoun, M.: Linear Regression for Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI) 32(11), 2106–2112 (2010)CrossRefGoogle Scholar
  18. 18.
    Hansen, L., Salamon, P.: Neural netwok ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 12(10), 993–1001 (1990)CrossRefGoogle Scholar
  19. 19.
    Kuncheva, L.: Combining Pattern Classi ers: Methods and Algorithms. Wiley, Hoboken (2004)CrossRefGoogle Scholar
  20. 20.
    Seal, A., Bhattacharjee, D., Nasipuri, M., Basu, D.K.: UGC-JU Face Database and its Benchmarking using Linear Regression Classier. Multimedia Tools and Applications (2013)Google Scholar
  21. 21.
    Bhattacharjee, D., Seal, A., Ganguly, S., Nasipuri, M., Basu, D.K.: A Comparative Study of Human thermal face recognition based on Haar wavelet transform (HWT) and Local Binary Pattern (LBP). Computational Intelligence and Neuroscience (2012)Google Scholar
  22. 22.
    Chen, Y.-T., Wang, M.-S.: Human Face Recognition Using Thermal Image. Journal of Medical and Biological Engineering 22(2), 97–102 (2002)Google Scholar
  23. 23.
    Morse, B.S.: Lecture 2: Image Processing Review, Neighbors. Connected Components, and Distance (1998-2004)Google Scholar
  24. 24.
    Everson, R., Karhunen, L.S.: Loeve procedure for gappy data. Journal of the Optical Society of America A 12(8), 1657–1664 (1995)CrossRefGoogle Scholar
  25. 25.
    Ardizzone, E., Cascia, M., Morana, M.: Probabilistic Corner Detection for Facial Feature Extraction. In: Proceedings of the 15th International Conference on Image Analysis and Processing, pp. 461–470, ISBN: 978-3-642-04145-7CrossRefGoogle Scholar
  26. 26.
    Alwakeel, M., Shaaban, Z.: Face Recognition Based on Haar Wavelet Transform and Principal Component Analysis via Levenberg-Marquardt Backpropagation Neural Network. European Journal of Scientic Research 42(1), 25–31 (2010) ISSN 1450-216XGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ayan Seal
    • 1
  • Debotosh Bhattacharjee
    • 1
  • Mita Nasipuri
    • 1
  • Consuelo Gonzalo-Martin
    • 2
  • Ernestina Menasalvas
    • 2
  1. 1.Computer Science and EngineeringJadavpur UniversityIndia
  2. 2.Center for Biomedical TechnologyUniversidad Politecnica de MadridSpain

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