Sleepy Eye's Recognition for Drowsiness Detection

Abstract

With the progress of science technology and the vehicle industry, there are much more vehicles on the road than ever before. As a result, the number of traffic accidents rises because of heavy traffic. Driver's inattention is usually the main reason that leads to a common traffic accident. To avoid this situation, this paper proposes a sleepy eye's recognition system for drowsiness detection. First, a cascaded Adaboost classifier with the Haar-like features is utilized to find out the face region. Second, the eyes region is located by Active Shape Models (ASM) search algorithm. Then the binary pattern and edge detection are adopted to extract the eyes feature and determine the eye's state. Experimental results demonstrate the comparative performance, even without the training stage.