Abstract
Several computational systems which depend on the precise location of the eyes have been developed in the last decades. Aware of this need, we propose a method for automatic detection of eyes in images of human faces using four geostatistical functions - semivariogram, semimadogram, covariogram and correlogram and support vector machines. The method was tested using the ORL human face database, which contains 400 images grouped in 40 persons, each having 10 different expressions. The detection obtained the following results of sensibility of 93.3%, specificity of 82.2% and accuracy of 89.4%.
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References
Almeida, J., Silva, A., Paiva, A.: Automatic Eye Detection Using Semivariogram Function and Support Vector Machine. In: 17th International Conference on Systems, Signals and Image Processing (2010)
Burges, C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Cambridge, A.L.: ORL Face Database (2009), Database available at http://www.cl.cam.ac.uk/research/dtg/attarchive/faceda-tabase.html
Chang, C., Lin, C.: LIBSVM: a library for support vector machines 80, 604–611 (2001), Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm
Clark, I.: Practical Geostatistics. Applied Sience Publishers, London (1979)
Gonzalez, R., Woods, R.: Digital image processing (2002) ISBN: 0-201-18075-8
Isaaks, E., Srivastava, R.: An introduction to applied geostatistics (1989)
Jiao, F., He, G.: Real-Time Eye Detection and Tracking under Various Light Conditions. Data Science Journal 6(0), 636–640 (2007)
Junior, O.S., Silva, A.C., Abdelouah, Z.: Personal Identification Based on Iris Texture Analysis Using Semivariogram and Correlogram Functions. International Journal for Computacional Vision and Biomechanics 2(1) (2009)
Kim, H.J., Kim, W.Y.: Eye Detection in Facial Images Using Zernike Moments with SVM. ETRI Journal 30(2), 335–337 (2008)
Lachenbruch, P.A., Goldstein, M.: Discriminant analysis. Biometrics, 69–85 (1979)
Motwani, M., Motwani, R., Jr, F.C.H.: Eye detection using wavelets and ann. In: Proceedings of Global Signal Processing Conferences & Expos for the Industry, GSPx (2004)
Peng, K., Chen, L., Ruan, S., Kukharev, G.: A robust algorithm for eye detection on gray intensity face without spectacles. Journal of Computer Science and Technology 5(3), 127–132 (2005)
Technologies, L.: SPSS for Windows vs 12.0. LEAD Technologies (2003)
Wang, P., Green, M., Ji, Q., Wayman, J.: Automatic eye detection and its validation. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 164–164 (2005)
Wang, Q., Yang, J.: Eye detection in facial images with unconstrained background. Journal of Pattern Recognition Research 1(1), 55 (2008)
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Almeida, J.D.S., Silva, A.C., Paiva, A.C. (2011). Automatic Eye Detection in Human Faces Using Geostatistical Functions and Support Vector Machines. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21596-4_16
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DOI: https://doi.org/10.1007/978-3-642-21596-4_16
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