Open-eye detection using iris–sclera pattern analysis for driver drowsiness detection

Abstract

This paper proposes a novel approach for open-eye detection that can be used in driver drowsiness analysis based on computer vision techniques. The proposed method captures the driver video using a low-resolution camera. The proposed drowsiness detection system has three main stages. The first stage is face detection using elliptical approximation and template matching techniques. In the second stage, the open eye is detected using the proposed iris–sclera pattern analysis method. In the third stage, the drowsiness state of the driver is determined using PERcentage of eye CLOSure (PERCLOS) measure. The entire system is designed to be independent of any specific data sets for face or eye detection. The proposed method for open-eye detection uses basic image processing concepts of morphological and laplacian operations. The proposed system was evaluated with real-life images and videos. Open-eye detection accuracy of 93% was achieved.