Automatic Eye Detection in Face Images for Unconstrained Biometrics Using Genetic Programming

  • Chandrashekhar Padole
  • Joanne Athaide
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8298)


Automatic extraction of eyes is a very important step in face detection and recognition system since eyes are one of the most stable features of the human face. In this paper, we present a novel technique using genetic programming for determining the classifier function to be used in the automatic detection of eyes in facial images. The feature terminals fed to the system are Gabor wavelet filtered image, mean, standard deviation and vertical position. Gabor wavelet transform has the optimal basis to extract local features. To find the Gabor wavelet to filter the image, we make use of Levenberg-Marquardt optimization. For the fitness function, we have used the concept of localization fitness, which is incorporated in the calculation of the precision and recall values to be included in fitness. We tested our system on the face images from the ORL databases and have presented our results. The result shows the effectiveness and flexibility provided by genetic programming in deciding the classifier for the detection of eyes in face images.


Fitness Function Genetic Programming Face Recognition Face Image Gabor Wavelet 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hassaballah, M., Ido, S.: Eye detection using and Appearance Information, MVA 2009 IAPR Conference on Machine Vision Applications, May 20-22, Yokohama, JAPAN (2009)Google Scholar
  2. 2.
    Wang, P., Green, M.B., Ji, Q., Wayman, J.: Automatic Eye Detection and Its Validation. In: Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPR Workshops 2005, p. 164. IEEE Computer Publication (2005)Google Scholar
  3. 3.
    Kim, H.-J., Kim, W.-Y.: Eye detection in facial images using Zernike moments with SVM. ETRI Journal 30(2), 335–337 (2008)CrossRefzbMATHGoogle Scholar
  4. 4.
    Zhou, Z.H., Geng, X.: Projection functions for eye detection. Pattern Recognition 37(5), 1049–1056 (2004)CrossRefzbMATHGoogle Scholar
  5. 5.
    Wang, J., Yin, L.: Eye Detection under Unconstrained Background by the Terrain Feature. In: IEEE International Conference on Multimedia and Expo, ICME 2005, pp. 1528–1531 (2005)Google Scholar
  6. 6.
    Zhu, Z., Ji, Q.: Robust real-time eye detection and tracking under variable lighting conditions and various face orientations. Journal on Computer Vision and Image Understanding - Special issue on Eye Detection and Tracking 98(1) (April 2005)Google Scholar
  7. 7.
    Wang, J., Yin, L.: Eye Detection Under Unconstrained Background by the Terrain Feature. In: IEEE International Conference on Multimedia and Expo, ICME, pp. 1528–1531 (2005)Google Scholar
  8. 8.
    Wang, Q., Yang, J.: Eye Location and Eye State Detection in Facial Images with Unconstrained Background. Journal of Information and Computing Science 1(5), 284–289 (2006)Google Scholar
  9. 9.
    Padole, C., Athaide, J.: Object Detection and Classification using Evolutionary Computations. International Journal on Science and Technology (2011)Google Scholar
  10. 10.
    Koza, J.R.: Genetic programming: on the programming of computers by means of natural selection. MIT Press, London (1992)zbMATHGoogle Scholar
  11. 11.
    Daubechies, I.: The Wavelet Transform, Time-Frequency Localization and Signal Analysis. IEEE Trans. Information Theory 36(5), 961–1004 (1990)CrossRefzbMATHMathSciNetGoogle Scholar
  12. 12.
    Daugman, J.G.: Two-Dimensional Spectral Analysis of Cortical Receptive Field Profile. Vision Research 20, 847–856 (1980)CrossRefGoogle Scholar
  13. 13.
    Daugman, J.G.: Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-Dimensional Visual Cortical Filters. J. Optical Soc. Amer. 2(7), 1160–1169 (1985)CrossRefGoogle Scholar
  14. 14.
    Zhang, M., Andreae, P., Pritchard, M.: Pixel statistics and false alarm area in genetic programming for object detection. In: Raidl, G.R., Cagnoni, S., Cardalda, J.J.R., Corne, D.W., Gottlieb, J., Guillot, A., Hart, E., Johnson, C.G., Marchiori, E., Meyer, J.-A., Middendorf, M. (eds.) EvoWorkshops 2003. LNCS, vol. 2611, pp. 455–466. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  15. 15.
    Goldstein, A.J., Harmon, L.D., Lesk, A.B.: Identification of Human Faces. Proc. IEEE 59(5), 748–760 (1971)CrossRefGoogle Scholar
  16. 16.
    Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The feret evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)CrossRefGoogle Scholar
  17. 17.
    Darwin, C.: On the origin of species: By Means of Natural Selection or the Preservation of Favoured Races in the Struggle for Life. Murray, London (1859)Google Scholar
  18. 18.
    Tabrizi, P.R., Zoroofi, R.A.: Open/Closed Eye Analysis for Drowsiness Detection. In: First Workshops on Image Processing Theory, Tools and Applications, IPTA 2008, pp. 1–7 (2008)Google Scholar
  19. 19.
    Bala, J., DeJong, K., Huang, J., Vafaie, H., Wechsler, H.: Visual routine for eye detection using hybrid genetic architectures. In: Proceedings of the 13th International Conference on Pattern Recognition, vol. 3, pp. 606–610 (1996)Google Scholar
  20. 20.
    Shen, L., Bai, L.: A review of Gabor wavelets for face recognition. Patt. Anal. Appl. 9, 273–292 (2006)CrossRefMathSciNetGoogle Scholar
  21. 21.
    Levenberg, K.: A method for the solution of certain problems in least squares. Quart. Appl. Math. 2, 164–168 (1944)zbMATHMathSciNetGoogle Scholar
  22. 22.
    Marquardt, D.: An algorithm for least-squares estimation of nonlinear parameters. SIAM J. Appl. Math. 11, 431–441 (1963)CrossRefzbMATHMathSciNetGoogle Scholar
  23. 23.
    Zhang, M.: Malcolm. Lett. Genetic Programming for Object Detection: Improving Fitness Functions and Optimising Training Data. IEEE Intelligent Informatics Bulletin (IEEE Computational Intelligence Bulletin) 7(1), 12–21 (2006)Google Scholar
  24. 24.
    Santos, G., Proença, H.: Periocular Biometrics: An Emerging technology for Unconstrained Scenarios. In: Proceedings of the IEEE Symposium on Computational Intelligence in Biometrics and Identity Management – CIBIM 2013, Singapore, April 16-19, pp. 14–21 (2013)Google Scholar
  25. 25.
    Padole, C.N., Proenca, H.: Periocular recognition: Analysis of performance degradation factors. In: 2012 5th IAPR International Conference on Biometrics (ICB), March 29-April 1, pp. 439–445 (2012)Google Scholar
  26. 26.
    Park, U., Jillela, R.R., Ross, A., Jain, A.K.: Periocular Biometrics in the Visible Spectrum. IEEE Transactions on Information Forensics and Security 6(1), 96–106 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Chandrashekhar Padole
    • 1
    • 2
  • Joanne Athaide
    • 1
    • 3
  1. 1.Technical ConsultantIRDC IndiaMumbaiIndia
  2. 2.Research Scholar at Dept. of InformaticsUniversity of Beira InteriorCovilhaPortugal
  3. 3.Design EngineerCoMira Solutions IncPittsburghUSA

Personalised recommendations