Eye state analysis using EyeMap for drowsiness detection

Drowsiness has become one of the many reasons of vehicular accidents. This research aims to create a system that can analyze whether the person is drowsy or non- drowsy and send a warning signal whenever it detects signs of drowsiness. This design undergoes several image processing for boosting the systems capability to retain only the region of interest and successfully initiate alarms within minimal time. It utilizes EyeMap mainly for eye localization and windowing and aided by the Circular Hough transform to extract only the eye region - specifically the iris; and classify whether the person is experiencing drowsiness at the moment. The researchers develop an additional device that is equipped with three warning signals and reacts on how the system sees the state of the person. Three setups were implemented in this study: Regular Camera, Infrared Sensitive Camera and Multiple Cameras. All setups were implemented during day and night to test the response of the system to varying lighting conditions. The subjects are tested inside a car and their present state is determined using the Karolinska Sleeping Scale. The current state of the person is then compared to the system's response. The subjects are tested three times under different setups to determine if the system is responding correctly under different condition. The study shows that the system is able to successfully determine whether the person is in the drowsy or non-drowsy state in all of the three setups, multi-camera being the mPost effective. However, it is limited by the capability of the camera to adapt to different lighting condition. During night time, the ability of the system to determine the state of the system drops.