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

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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.



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


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