We actualized a fatigue driver recognition framework utilizing a mix of driver's state and driving conduct pointers. For driver's express, the framework observed the eyes' blinking rate and the flickering span. Fatigue drivers have these qualities higher than ordinary levels. We utilized a camera with machine vision procedures to find out and watch driver's blinking behavior. Harr's feature classifier was utilized to first find the eye's range, and once found, a layout coordinating was utilized to track the eye for fast preparing. For driving conduct, we gained the vehicle's state from inertial measurement unit and gas pedal sensors. The principle component analysis was utilized to choose the components that have high change. The difference esteems were utilized to separate weakness drivers, which are accepted to have higher driving exercises, from typical drivers.