Thermal Face Recognition Using Local Interest Points and Descriptors for HRI Applications

  • Gabriel Hermosilla
  • Patricio Loncomilla
  • Javier Ruiz-del-Solar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6556)


In this article a robust thermal face recognition methodology based on the use of local interest points and descriptors, is proposed. The methodology consists of the following stages: face segmentation, vascular network detection, wide baseline matching using local interest points and descriptors, and classification. The main contribution of this work is the use of a standard wide baseline matching methodology for the comparison of vascular networks from thermal face images. The proposed methodology is validated using a database of thermal images. This work could be of high interest for HRI applications related with the visual recognition of humans, as the ones included in the RoboCup @Home league, because the use of thermal images may overcome limitations such as dependency on illumination conditions and facial expressions.


Face Recognition Thermal Images Blood Vessels Matching SIFT Matching RoboCup @Home 


  1. 1.
    Sinha, P., Balas, B., Ostrovsky, Y., Russell, R.: Face Recognition by Humans: 19 Results All Computer Vision Researchers Should Know About. Proc. of the IEEE 94(11), 1948–1962 (2006)CrossRefGoogle Scholar
  2. 2.
    Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J.: Face Recognition: A Literature Survey. ACM Computing Surveys, 399–458 (2003)Google Scholar
  3. 3.
    Tan, X., Chen, S., Zhou, Z.-H., Zhang, F.: Face recognition from a single image per person: A survey. Pattern Recognition 39, 1725–1745 (2006)CrossRefzbMATHGoogle Scholar
  4. 4.
    Chellappa, R., Wilson, C.L., Sirohey, S.: Human and Machine Recognition of Faces: A Survey. Proceedings of the IEEE 83(5), 705–740 (1995)CrossRefGoogle Scholar
  5. 5.
    Face Recognition Homepage (January 2008),
  6. 6.
    Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neurosicence 3(1), 71–86 (1991)CrossRefGoogle Scholar
  7. 7.
    Ruiz-del-Solar, J., Verschae, R., Correa, M.: Recognition of Faces in Unconstrained Environments: A Comparative Study. EURASIP Journal on Advances in Signal Processing (Recent Advances in Biometric Systems: A Signal Processing Perspective) 2009, article ID 184617, 19 pages (2009)zbMATHGoogle Scholar
  8. 8.
    Socolinsky, D.A., Selinger, A.: A comparative Analysis of face recognition performance with visible and thermal infrared imagery. In: Proc. ICPR 2002, Quebec, Canada (August 2002)Google Scholar
  9. 9.
    Selinger, A., Socolinsky, D.: Appearance-Based Facial Recognition Using Visible and Thermal Imagery: A Comparative Study, Tech. Rep., Equinox Corporation (2001)Google Scholar
  10. 10.
    Desa, S., Hati, S.: IR and Visible Face Recognition using Fusion of Kernel Based Features. In: ICPR 2008, pp. 1–4 (2008)Google Scholar
  11. 11.
    Ferrari, V., Tuytelaars, T., Van Gool, L.: Simultaneous Object Recognition and Segmentation by Image Exploration. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 40–54. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proc. 4th Alvey Vision Conf., Manchester, UK, pp. 147–151 (1998)Google Scholar
  13. 13.
    Lowe, D.: Local feature view clustering for 3D object recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, Hawaii, pp. 682–688. IEE Press, New York (2001)Google Scholar
  14. 14.
    Lowe, D.: Distinctive Image Features from Scale-Invariant Keypoints. Int. Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  15. 15.
    Loncomilla, P., Ruiz-del-Solar, J.: A Fast Probabilistic Model for Hypothesis Rejection in SIFT-Based Object Recognition. In: Martínez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds.) CIARP 2006. LNCS, vol. 4225, pp. 696–705. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Mikolajczyk, K., Schmid, C.: Scale & Affine Invariant Interest Point Detectors. Int. Journal of Computer Vision 60(1), 63–96 (2004)CrossRefGoogle Scholar
  17. 17.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Machine Intell. 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  18. 18.
    Buddharaju, P., Pavlidis, I.: Multi-Spectral Face Recognition - Fusion of Visual Imagery with Physiological Information. In: Face Biometrics for Personal Identification: Multi-Sensory Multi-Modal Systems, pp. 91–108. Springer, Heidelberg (January 2007)CrossRefGoogle Scholar
  19. 19.
    Buddharaju, P., Pavlidis, I., Manohar, C.: ‘Face Recognition Beyond the Visible Spectrum. In: Advances in Biometrics: Sensors, Algorithms and Systems, pp. 157–180. Springer, Heidelberg (October 2007)Google Scholar
  20. 20.
    Hermosilla, G., Ruiz-del-Solar, J., Verschae, R., Correa, M.: Face Recognition using Thermal Infrared Images for Human-Robot Interaction Applications: A Comparative Study. In: 6th IEEE Latin American Robotics Symposium – LARS 2009, Valparaíso, Chile (CD Proceedings) (October 29-30, 2009)Google Scholar
  21. 21.
    Ruiz del Solar, J., Loncomilla, P.: Robot Head Pose Detection and Gaze Direction Determination Using Local Invariant Features. Advanced Robotics 23(3), 305–328 (2009)CrossRefGoogle Scholar
  22. 22.

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gabriel Hermosilla
    • 1
    • 2
  • Patricio Loncomilla
    • 1
    • 2
  • Javier Ruiz-del-Solar
    • 1
    • 2
  1. 1.Department of Electrical EngineeringUniversidad de ChileChile
  2. 2.Center for Mining TechnologyUniversidad de ChileChile

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