A Learning-based Eye Detector Coupled with Eye Candidate Filtering and PCA Features
Bruno Marangoni dos Santos Leite, Eanes Torres Pereira, Herman Martins Gomes, Luciana Veloso, Cicero Einstein do Nascimento Santos, J.M. de Carvalho · 2007
In this work, we present a system based on a Neural Network classifier for eye detection in human face images. This classifier works on eye candidate regions extracted from a face image and represented by a reduced number of features, selected by Principal Component Analysis. The regions are determined considering that in an image window containing the eye, the grey level distribution will generally assume a pattern of adjacent light-dark-light horizontal and vertical stripes, corresponding to the eyelid, pupil and eyelid, respectively. For training, validation and testing, a database was built with a total of 4,400 images. Experimental results have shown that the proposed approach correctly detects more eyes than any of two existing systems (Rowley-Baluja-Kanade and Machine Perception Toolbox), for eye location error tolerances from 0 to 5 pixels. Considering an error tolerance of 9 pixels, the correct detection rate achieved was above 90%.