Driver fatigue is one of the leading causes of traffic accidents.

Therefore, the use of assistive systems that monitor a driver’s

level of vigilance and alert the driver in case of drowsiness

and distraction can be significant in the prevention of

accidents. This paper presents morphology based operations

in extracting various visual cues like eye, eye brows, mouth

and head movement. The parameters used for detecting

fatigue are: eye closure duration measured through eye state

information, head movement through orientation of head

ellipse and yawning analyzed through mouth state

information. This system was validated with synthetic data

under real-life fatigue conditions with human subjects of

different ethnic backgrounds, genders, and ages and under

different illumination conditions. It was found to be

reasonably robust, reliable, and accurate in fatigue

characterization-Driver fatigue is one of the leading causes of traffic accidents.

Therefore, the use of assistive systems that monitor a driver’s

level　of　vigilance　and　alert　the　driver　in　case　of　drowsiness

and　distraction　can　be　significant　in　the　prevention　of

accidents.　This　paper　presents　morphology　based　operations

in extracting　various visual cues like　eye, eye brows,　mouth

and　head　movement.　The　parameters　used　for　detecting

fatigue are:　eye closure duration　measured through eye　state

information,　head　movement　through　orientation　of　head

ellipse　and　yawning　analyzed　through　mouth　state

information.　This　system　was　validated　with　synthetic　data

under　real-life　fatigue　conditions　with　human　subjects　of

different　ethnic　backgrounds,　genders,　and　ages　 and　under

different　illumination　conditions.　 It　was　found　to　be

reasonably　robust,　reliable,　and　accurate　in　fatigue

characterization