Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Digital Image Processing
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Automatic Eye Detection and Its Validation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Editorial: Special issue: eye detection and tracking
Computer Vision and Image Understanding - Special issue on eye detection and tracking
International Journal of Data Analysis Techniques and Strategies
Classification of breast tissues using Getis-Ord statistics and support vector machine
Intelligent Decision Technologies - Special issue on advances in medical intelligent decision support systems
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Several computational systems that depend on the precise location of the eyes were developed in the last decades. Aware of this need, we propose a method for automatic eye detection in images of human faces using geostatistical functions (semivariogram, Getis index, Moran's index and Geary's coefficient) and support vector machines. The geostatistical measures are used as input features for a support vector machine classifier with the purpose of distinguishing patterns of eyes region and other areas of the face. The method was tested with ORL human face database, which contains 400 images of 40 persons, having ten different expressions for each person. The use of the proposed techniques showed to be very promising, since we obtained results of sensitivity of 92.2% for Moran's index, specificity of 93.4% and accuracy of 88.45% for semivariogram function.