A robust and accurate real time eye tracking system has been a challenging task for many computer vision applications. Different researchers working world wide have tried various approaches to solve this problem. Although many different algorithms exist to perform eye detection, each has its own weaknesses and strengths. But so far no system / technique exists which has shown satisfactory results in all circumstances. This research work is a comparative study on the performances of algorithms

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... emplate Matching, Skin Segmentation, Artificial Neural Network and Haar Cascade Classifier for eye recognition. All the algorithms are developed on OpenCV platform and tested on images from Mathworks Video, GTAV, Face Expression and VITS database in the laboratory. The comparison is done based on the success rate i.e. total number of images with eyes detected to the total number of input images. The comparison results show that Haar Cascade Classifier has satisfactory results on images under different conditions such as tilted head position, closed eyes, occluded face, etc., .The purpose of this research work is to develop a Non-intrusive Driver's Drowsiness detection system based on eye blink rate for preventing accidents on road.