Improving Face Segmentation in Thermograms Using Image Signatures

  • Sílvio Filipe
  • Luís A. Alexandre
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6419)


The aim of this paper is to present a method for the automatic segmentation of face images captured in (LWIR), allowing for a large range of face rotations and expressions. The motivation behind this effort is to enable better performance of face recognition methods in the thermal (IR) images. The proposed method consists on the modelling of background and face pixels by two normal distributions each, followed by a post-processing step of face dilation for closing holes and delimitation based on vertical and horizontal images signatures. Our experiments were performed on images of the (UND) and (FSU) databases. The obtained results improve on previous existing methods from 2.8% to more than 25% depending on the method and database.


Face Segmentation Human Skin Segmentation Image segmentation Infrared Thermal 


  1. 1.
    Bowyer, K., Chang, K., Flynn, P.: A survey of approaches to three-dimensional face recognition. In: 17th International Conference on Pattern Recognition (ICPR 2004), pp. 358–361 (2004)Google Scholar
  2. 2.
    Chen, X., Flynn, P., Bowyer, K.: IR and visible light face recognition. Computer Vision and Image Understanding 99, 332–358 (2005)CrossRefGoogle Scholar
  3. 3.
    Cho, S., Wang, L., Ong, W.: Thermal imprint feature analysis for face recognition. In: IEEE International Symposium on Industrial Electronics (ISlE), pp. 1875–1880Google Scholar
  4. 4.
    Flynn, P., Bowyer, K., Phillips, P.: Assessment of Time Dependency in Face Recognition: An Initial Study. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 44–51. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Gyaourova, A., Bebis, G., Pavlidis, I.: Fusion of infrared and visible images for face recognition. In: Pajdla, T., Matas, J. (eds.) ECCV 2004, Part IV. LNCS, vol. 3024, pp. 456–468. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Jain, A., Flynn, P., Ross, A.: Handbook of Biometrics. Springer, New York (2007)Google Scholar
  7. 7.
    Kong, S., Heo, J., Abidi, B., Paik, J., Abidi, M.: Recent advances in visual and infrared face recognition - a review. Computer Vision and Image Understanding (1), 103–135Google Scholar
  8. 8.
    Pavlidis, I., Tsiamyrtzis, P., Manohar, C., Buddharaju, P.: Biometrics: Face recognition in thermal infrared. ch. 29, pp. 1–15. CRC Press, Boca Raton (2006)Google Scholar
  9. 9.
    Srivastava, A., Liu, X.: Statistical hypothesis pruning for identifying faces from infrared images. Image and Vision Computing, 651–661Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Sílvio Filipe
    • 1
  • Luís A. Alexandre
    • 1
  1. 1.Department of Computer Science, IT - Instituto de Telecomunicações, SOCIA - Soft Computing and Image Analysis GroupUniversity of Beira InteriorCovilhãPortugal

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