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

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Reference

Chandrashekhar Padole and Joanne Athaide: Automatic Eye Detection in Face Images for Unconstrained Biometrics Using Genetic Programming. Proceedings of the 4th International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO 2013), Part II, Lecture Notes in Computer Science, Vol. 8298, pp. 364-375, Springer, December 19-21 2013.

DOI

http://dx.doi.org/10.1007/978-3-319-03756-1_33

Abstract

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.

Extended Abstract

Bibtex

Used References

Hassaballah, M., Ido, S.: Eye detection using and Appearance Information, MVA 2009 IAPR Conference on Machine Vision Applications, May 20-22, Yokohama, JAPAN (2009)

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)

Kim, H.-J., Kim, W.-Y.: Eye detection in facial images using Zernike moments with SVM. ETRI Journal 30(2), 335–337 (2008) http://dx.doi.org/10.4218/etrij.08.0207.0150

Zhou, Z.H., Geng, X.: Projection functions for eye detection. Pattern Recognition 37(5), 1049–1056 (2004) http://dx.doi.org/10.1016/j.patcog.2003.09.006

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)

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)

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)

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)

Padole, C., Athaide, J.: Object Detection and Classification using Evolutionary Computations. International Journal on Science and Technology (2011)

Koza, J.R.: Genetic programming: on the programming of computers by means of natural selection. MIT Press, London (1992)

Daubechies, I.: The Wavelet Transform, Time-Frequency Localization and Signal Analysis. IEEE Trans. Information Theory 36(5), 961–1004 (1990) http://dx.doi.org/10.1109/18.57199

Daugman, J.G.: Two-Dimensional Spectral Analysis of Cortical Receptive Field Profile. Vision Research 20, 847–856 (1980) http://dx.doi.org/10.1016/0042-6989(80)90065-6

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) http://dx.doi.org/10.1364/JOSAA.2.001160

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) http://dx.doi.org/10.1007/3-540-36605-9_42

Goldstein, A.J., Harmon, L.D., Lesk, A.B.: Identification of Human Faces. Proc. IEEE 59(5), 748–760 (1971) http://dx.doi.org/10.1109/PROC.1971.8254

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) http://dx.doi.org/10.1109/34.879790

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)

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)

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)

Shen, L., Bai, L.: A review of Gabor wavelets for face recognition. Patt. Anal. Appl. 9, 273–292 (2006) http://dx.doi.org/10.1007/s10044-006-0033-y

Levenberg, K.: A method for the solution of certain problems in least squares. Quart. Appl. Math. 2, 164–168 (1944)

Marquardt, D.: An algorithm for least-squares estimation of nonlinear parameters. SIAM J. Appl. Math. 11, 431–441 (1963) http://dx.doi.org/10.1137/0111030

hang, 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)

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)

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)

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) http://dx.doi.org/10.1109/TIFS.2010.2096810


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