A Driver Face Monitoring System for Fatigue and Distraction Detection

Abstract

Driver face monitoring system is a real-time system that can detect driver fatigue and distraction using machine vision approaches. In this paper, a new approach is introduced for driver hypovigilance (fatigue and distraction) detection based on the symptoms related to face and eye regions. In this method, face template matching and horizontal projection of top-half segment of face image are used to extract hypovigilance symptoms from face and eye, respectively. Head rotation is a symptom to detect distraction that is extracted from face region. The extracted symptoms from eye region are (1) percentage of eye closure, (2) eyelid distance changes with respect to the normal eyelid distance, and (3) eye closure rate. The first and second symptoms related to eye region are used for fatigue detection; the last one is used for distraction detection. In the proposed system, a fuzzy expert system combines the symptoms to estimate level of driver hypo-vigilance. There are three main contributions in the introduced method: (1) simple and efficient head rotation detection based on face template matching, (2) adaptive symptom extraction from eye region without explicit eye detection, and (3) normalizing and personalizing the extracted symptoms using a short training phase. These three contributions lead to develop an adaptive driver eye/face monitoring. Experiments show that the proposed system is relatively efficient for estimating the driver fatigue and distraction.

A Review on Driver Face Monitoring Systems for Fatigue and Distraction Detection

“…Due to the simple extracted features and selection of the best features, this algorithm is relatively fast and robust. This algorithm was used in [7,26,27,28,29,44,51,59,74] for face detection.…”

“…In [22,51,59], eyelid distance was used for drowsiness detection. Distance between eyelids is decreased when driver is fatigue.…”

A Review on Fatigue Driving Detection

“…When the driver is tired, the blink frequency will be higher than the normal value. Sigari et al [27] used the computer vision technology to extract the driver's eye information and calculated the blink frequency. The calculated blink frequency was used to analyse the driver's fatigue.…”

Development of information technology for the generation and maintenance of knowledge–oriented control systems

“…An expert system REXPERT [6] is used in training students to control their knowledge. In paper [7], an expert system for monitoring level of driver vigilance is described. Knowledge-oriented systems are known that allow monitoring dangerous situations, particularly on roads [8].…”