Are Haar-Like Rectangular Features for Biometric Recognition Reducible?

  • Kamal Nasrollahi
  • Thomas B. Moeslund
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)


Biometric recognition is still a very difficult task in real-world scenarios wherein unforeseen changes in degradations factors like noise, occlusion, blurriness and illumination can drastically affect the extracted features from the biometric signals. Very recently Haar-like rectangular features which have usually been used for object detection were introduced for biometric recognition resulting in systems that are robust against most of the mentioned degradations [9]. The problem with these features is that one can define many different such features for a given biometric signal and it is not clear whether all of these features are required for the actual recognition or not. This is exactly what we are dealing with in this paper: How can an initial set of Haar-like rectangular features, that have been used for biometric recognition, be reduced to a set of most influential features? This paper proposes total sensitivity analysis about the mean for this purpose for two different biometric traits, iris and face. Experimental results on multiple public databases show the superiority of the proposed system, using the found influential features, compared to state-of-the-art biometric recognition systems.


  1. 1.
    Chesnokov, Y.: Face Detection C++ Library with Skin and Motion Analysis. Biometrics AIA 2007 TTS (2007)Google Scholar
  2. 2.
    Choi, J., Juneho, Y., Turk, M.: Effective representation using ICA for face recognition robust to local distortion and partial occlusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(12), 1977–1981 (2005)CrossRefGoogle Scholar
  3. 3.
    Etemad, K., Chellappa, R.: Discriminant analysis for recognition of human face images. Journal of the Optical Society of America A 14(8), 1724–1733 (1997)CrossRefGoogle Scholar
  4. 4.
    Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6), 643–660 (2001)CrossRefGoogle Scholar
  5. 5.
    Hotta, K.: Robust face recognition under partial occlusion based on support vector machine with local Gaussian summation kernel. Image and Vision Computing 26(11), 1490–1498 (2008)CrossRefGoogle Scholar
  6. 6.
    Kumar, A., Hanmandlu, M., Das, A., Gupta, H.M.: Biometric based personal authentication using fuzzy binary decision tree. In: Proceedings of 5th IAPR International Conference on Biometrics, pp. 396–401 (2012)Google Scholar
  7. 7.
    Kumar, A., Passi, A.: Comparison and combination of iris matchers for reliable personal authentication. Pattern Recognition 43(3), 1016–1026 (2010)CrossRefzbMATHGoogle Scholar
  8. 8.
    Ma, L., Wang, C., Xiao, B., Zhou, W.: Sparse representation for face recognition based on discriminative low-rank dictionary learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2586–2593 (2012)Google Scholar
  9. 9.
    Nasrollahi, K., Moeslund, T.B., Rashidi, M.: Haar-like Rectangular Features for Biometric Recognition. In: Proceedings of 6th IAPR International Conference on Biometrics (2013)Google Scholar
  10. 10.
    Ngoc-Son, V.: Exploring Patterns of Gradient Orientations and Magnitudes for Face Recognition. IEEE Transactions on Information Forensics and Security 8(2), 295–304 (2013)CrossRefGoogle Scholar
  11. 11.
    Rivera, A.R., Castillo, J.R., Chae, O.: Local Directional Number Pattern for Face Analysis: Face and Expression Recognition. IEEE Transactions onImage Processing 22(5), 1740–1752 (2013)CrossRefGoogle Scholar
  12. 12.
    Specht, D.F.: Probabilistic neural networks. Neural Networks 3, 109–118 (1990)CrossRefGoogle Scholar
  13. 13.
    Szewczyk, R., Grabowski, K., Napieralska, M., Sankowski, W., Zubert, M., Napieralski, A.: A reliable iris recognition algorithm based on reverse biorthogonal wavelet transform. Pattern Recognition Letters 33(8), 1019–1026 (2012)CrossRefGoogle Scholar
  14. 14.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  15. 15.
    Viola, P., Jones, M.: Rapid object detection using a boosted classifier of simple features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1(1), pp. 511–518 (2001)Google Scholar
  16. 16.
    Xiaomin, L., Peihua, L.: Tensor decomposition of SIFT descriptors for person identification. In: IAPR International Conference on Biometrics, pp. 265–270 (2012)Google Scholar
  17. 17.
    Xiaoyang, T., Triggs, B.: Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions. IEEE Transactions on Image Processing 19(6), 1635–1650 (2010)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Yooyoung, L., Filliben, J.J., Micheals, R.J., Phillipstitle, P.J.: Sensitivity analysis for biometric systems: A methodology based on orthogonal experiment designs. Computer Vision and Image Understanding 117(5), 532–550 (2013)CrossRefGoogle Scholar
  19. 19.
    Yung-Hui, L., Savvides, M.: An Automatic Iris Occlusion Estimation Method Based on High-Dimensional Density Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(4), 784–796 (2013)CrossRefGoogle Scholar
  20. 20.
    Zhu, Z., Morimoto, T., Adachi, H., Kiriyama, O., Koide, T., Mattausch, H.J.: Multi-view face detection and recognition using haar-like features. In: Proceedings of COE Workshop (2006)Google Scholar
  21. 21.
    Chengqiang, L., Mei, X.: Iris Recognition Based on DLDA. In: Proceedings of 18th International Conference on Pattern Recognition, vol. 4, pp. 489–492 (2006)Google Scholar
  22. 22.
    ATT Laboratories Cambridge, The ORL Database of Faces,
  23. 23.
    Collection of Facial Images: Faces94,
  24. 24.
    Image Engineering Laboratory, The Sheffield UMIST Face Database,

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kamal Nasrollahi
    • 1
  • Thomas B. Moeslund
    • 1
  1. 1.Visual Analysis of People LaboratoryAalborg UniversityAalborgDenmark

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