Pattern Analysis and Applications

, Volume 13, Issue 2, pp 197–211 | Cite as

The architecture and performance of the face and eyes detection system based on the Haar cascade classifiers

  • Andrzej Kasinski
  • Adam Schmidt


The precise face and eyes detection is essential in many human–machine interface systems. Therefore, it is necessary to develop a reliable and efficient object detection method. In this paper we present the architecture of a hierarchical face and eyes detection system using the Haar cascade classifiers (HCC) augmented with some simple knowledge-based rules. The influence of the training procedure on the performance of the particular HCCs has been investigated. Additionally, we compared the efficiency of other authors’ face and eyes HCCs with the efficiency of those trained by us. By applying the proposed system to the set of 10,000 test images we were able to properly detect and precisely localize 94% of the eyes.


Face detection Eyes detection Haar cascade classifiers 


  1. 1.
    Campadelli P, Lanzarotti R, Lipori G (2006) Eye localization: a survey. In: Esposito A et al (eds) Fundamentals of verbal and nonverbal communication and the biometric issue. IOS Press BV, Amsterdam, pp 234–245Google Scholar
  2. 2.
    Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE conference on computer vision and pattern recognition 2001, pp 511–518)Google Scholar
  3. 3.
    Kotropoulos C, Pitas I (1997) Rule-based face detection in frontal views. In: Proceedings of the international conference on acoustics, speech and signal processing 1997, pp 2537–2540Google Scholar
  4. 4.
    Hsu R-L, Abdel-Mottaleb M, Jain A (2002) Face detection in color images. IEEE Trans Pattern Anal 24:696–706CrossRefGoogle Scholar
  5. 5.
    Heisele B, Serre T, Pontil M, Poggio T (2001) Component-based face detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition 2001, pp 657–662Google Scholar
  6. 6.
    Bileschi S, Heisele B (2003) Advances in component based face detection. In: Proceedings of the IEEE workshop on analysis and modelling of faces and gestures 2003, pp 149–156Google Scholar
  7. 7.
    Su M-C, Chou C-H (2001) Associative-memory-based human face detection. IEICE Trans Inform Syst E84-D:1067–1074Google Scholar
  8. 8.
    Rowley H, Kanade T, Baluja S (1996) Neural network-based face detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition 1996, pp 203–207Google Scholar
  9. 9.
    Huang L-L, Shimizu A, Hagihara Y, Kobatake H (2003) Gradient feature extraction for classification-based face detection. Pattern Recognit 36:2501–2511CrossRefGoogle Scholar
  10. 10.
    Lienhart R, Kuranov A, Pisarevsky V (2002) Empirical analysis of detection cascades of boosted classifiers for rapid object detection. Intel Labs, Microprocessor Research Lab Technical reportGoogle Scholar
  11. 11.
    Meynet J, Popovici V, Thiran J-P (2005) Face detection with mixtures of boosted discriminant features. Ecole Polytechnique Fédérale de Lausanne Signal Processing Institute Technical reportGoogle Scholar
  12. 12.
    Wang Q, Yang J (2006) Eye detection in facial images with unconstrained background. J Pattern Recognit Res 1:55–62Google Scholar
  13. 13.
    Kumar T, Raja K, Ramakrishnan A (2002) Eye detection using color cues and projection functions. In: Proceedings of the IEEE international conference on image processing 2002, pp 337–340Google Scholar
  14. 14.
    Peng K, Chen L, Ruan S, Kukharev G (2005) A robust algorithm for eye detection on gray intensity face without spectacles. J Comput Sci Technol 5:127–132Google Scholar
  15. 15.
    Wu J, Zhou Z-H (2003) Efficient face candidates selector for face detection. Pattern Recognit 36:1175–1186CrossRefGoogle Scholar
  16. 16.
    Campadelli P, Lanzarotti R, Lipori G (2006) Precise eye localization through a general-to-specific model definition. In: Proceedings of the 7th British machine vision conference, pp 187–196Google Scholar
  17. 17.
    Motwani M, Motwani R, Harris F (2004) Eye detection using wavelets and ANN. In: Proceedings of the global signal processing expo and conference 2004Google Scholar
  18. 18.
    Tivive F, Bouzerdoum A (2005) A fast neural-based eye detection system. University of Wollongong, Faculty of Informatics Technical reportGoogle Scholar
  19. 19.
    Bianchini M, Sarti L (2006) An eye detection system based on neural autoassociators. In Proceedings of the IAPR international workshop on artificial neural networks in pattern recognition 2006, pp 244–252Google Scholar
  20. 20.
    Wilson P, Fernandez J (2006) Facial features detection using Haar classifiers. J Comput Sci Coll 21:127–133Google Scholar
  21. 21.
    Feng X, Wang Y, Li B (2006) A fast eye location method using ordinal features. In: Proceedings of the ACM SIGCHI international conference on advances in computer entertainment technology 2006, p 95Google Scholar
  22. 22.
    Wang P, Green M, Ji Q, Wayman J (2005) Automatic eye detection and its validation. In: Proceedings of the IEEE conference on computer vision and pattern recognition 2005, p 164Google Scholar
  23. 23.
    Everingham M, Zisserman A (2006) Regression and classification approaches to eye localization in face images. In: Proceedings of the international conference on automatic face and gesture recognition, pp 441–448Google Scholar
  24. 24.
    Arandjelovic O, Zisserman A (2005) Automatic face recognition for film character retrieval in feature-length films. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 860–867Google Scholar
  25. 25.
    Freund Y, Schapire R (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139zbMATHCrossRefMathSciNetGoogle Scholar
  26. 26.
    Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Wadsworth International Group, BelmontzbMATHGoogle Scholar
  27. 27.
    Friedman, Hastie T, Tibshirani R (1998) Additive logistic regression: a statistical view of boosting. Stanford University, Department of Statistics, Technical ReportGoogle Scholar
  28. 28.
    Open Computer Vision Library.
  29. 29.
    Castrillón-Santana M, Lorenzo-Navarro J, Déniz-Suárez O, Falcón-Martel A (2005) Multiple face detection at different resolutions for perceptual user interfaces. In: Proceedings of the 2nd Iberian conference on pattern recognition and image analysis, pp 445–452Google Scholar

Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  1. 1.Institute of Control and Information EngineeringPoznan University of TechnologyPoznanPoland

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