Real-Time Vision Based Driver Drowsiness Detection Using Partial Least Squares Analysis

Robust eye state classification in real-time is very crucial for automatic driver drowsiness detection to avoid road accidents. In this paper, we propose partial least squares (PLS) analysis based eye state classification method and its real-time implementation on resource constraint digital video processor platform, to monitor the eye state during all time driving conditions. The drowsiness is detected using percentage of eye closure (PERCLOS) metric. In this approach, face in the infrared (IR) image is detected using Haar features based cascaded classifier and within the face, eye is detected. For binary eye state classification, PLS analysis is applied to obtain the low dimensional discriminative subspace, within which simple PLS regression score based classifier is used to classify test vector into open and closed. We compared our algorithm to recent methods on challenging test sequences and the result shows superior performance. The results obtained during on-vehicle testing show that the proposed system achieves significant improvement in classification accuracy at nearly 3 frames per second.