EYE-TRACKING FOR DETECT

In this paper, we describe a system that locates and tracks the eyes of a driver. The purpose of such a system is to perform detection of driver fatigue. By mounting a small camera inside the car, we can monitor the face of the driver and look for eye-movements which indicate that the driver is no longer in condition to drive. In such a case, a warning signal should be issued. This paper describes how to find and track the eyes. We also describe a method that can determine if the eyes are open or closed. The primary criterion for the successful implementation of this system is that it must be highly nonintrusive. The system should start when the ignition is turned on without having the driver initiate the system. Nor should the driver be responsible for providing any feedback to the system. The system must also operate regardless of the texture and the color of the face. It must also be able to handle diverse conditions, such as changes in light, shadows, reflections, etc. INTRODUCTION Driver fatigue is an important factor in a large number of accidents. Lowering the number of fatigue-related accidents would not only save society a significant amount financially, but also reduce personal suffering. We believe that by monitoring the eyes, the symptoms of driver fatigue in our proposed system can be detected early enough to avoid several of these accidents. Detection of fatigue involves a sequence of images of a face, and observation of eyemovements and blink patterns. The analysis of face images is a popular research area with applications such as face recognition, virtual tools and handicap aids [9,14], human identification and database retrieval [ 3 ] , There are also many real-time systems, being developed in order to track face features [15,13,17]. These kinds of real-time systems generally consist of three components: a) Localization of the eyes (in the first frame), b) Tracking the eyes in the subsequent frames, c) Detection of failure in tracking. Localization of the eyes involves looking at the entire image of the face and determining the eye-envelopes (the areas around the eyes). During tracking in subsequent frames, the search-space is reduced to the area corresponding to the eye-envelopes in the current frame. This tracking can be done at relatively low computational effort, since the search-space is significantly reduced. In order to detect failure in the tracking, general constraints such as distance between the eyes and horizontal alignment of the two eyes can be used. This paper is organized as follows: In the next section, we describe some of the previous work in this area. Afterwards, we describe the experimental setup, and how the system operates. Then, we proceed to the description of the algorithm for the detection of fatigue. Finally, we present results and future work. PREVIOUS W Many methods have been proposed for localizing facial features in images [2,4,5,6,8,10,11,12,19]. These methods can roughly be divided into two categories: Template-based matching and Feature-based matching. These techniques are compared by Poggio and Brunelli [ 11. One popular templatematching technique for extraction of face features is to use deformnhle templates [5,16], which are similar to the active snakes introduced by Kass [7], in the sense that they apply energy 314 0-7803~4269-0/97/~10.0