Measurement of Eye Blinking Through Intel Microprocessor for Safety Driving

Driver in-alertness is an important cause for most accidents related to vehicle crashes. Drowsy driver detection methods can form the basis of a system to potentially reduce accidents related to driver doziness. Intel-Eye describes a real-time driver in alertness and shock related facial expression monitoring. Intel -Eye obtains visual cues such as eyelid movement; gaze movement, head movement, and facial expression that typically characterize the level of alertness of a person are extracted in real time and systematically combined to infer the fatigue level of the driver. Intel-eye distinguishes itself by the Two-Way Approach in eye gaze analysis. Shock analysis is done to identify the driver’s expression and signals are sent for automatic braking system. A probabilistic model is developed to model in Intel-Eye and it is used for predicting human in-alertness based on the visual cues obtained. But this model is mainly focus on eye blinking because this system is connected with programming by using METLAB. I have used MATLAB programming to make an automatic eye blink tracking and detection system for a video. The eyes are tracking by image sequence, the eyes are tracked and correlation scores between the actual eye and the corresponding “closed-eye” templates, which are used to detect blinks. Accurate head and eye tracking results are obtained at a processing rate of more than 30 frames per second (fps), in more than 90% cases with a low false positive blink detection of 0.01%. I take ideal eye blinking rate in first 10 minutes in driving of human, then we observe the changes rate in blinking frequencies. We observed that the dangerous condition occurs when the eye blinking rate are decreases (increase) as 50% (50%), 75% (100%), 100% (300%) from natural condition for lower , medium and higher level dangerous conditions respectively.