Feed Forward Neural Network Based Eye Localization and Recognition Using Hough Transform

Abstract

Eye detection is a pre-requisite stage for many applications such as face recognition, iris recognition, eye tracking, fatigue detection based on eye-blink count and eye-directed instruction control. As the location of the eyes is a dominant feature of the face it can be used as an input to the face recognition engine. In this direction, the paper proposed here localizes eye positions using Hough Transformed (HT) coefficients, which are found to be good at extracting geometrical components from any given object. The method proposed here uses circular and elliptical features of eyes in localizing them from a given face. Such geometrical features can be very efficiently extracted using the HT technique. The HT is based on a evidence gathering approach where the evidence is the ones cast in an accumulator array. The purpose of the technique is to find imperfect instances of objects within a certain class of shapes by a voting procedure. Feed forward neural network has been used for classification of eyes and non-eyes as the dimension of the data is large in nature. Experiments have been carried out on standard databases as well as on local DB consisting of gray scale images. The outcome of this technique has yielded very satisfactory results with an accuracy of 98.68%