Mobile-Based Eye-Blink Detection Performance Analysis on Android Platform

In this article, we develop a real-time mobile phone-based gaze tracking and eye-blink detection system on Android platform. Our eye-blink detection scheme is developed based on the time difference between two open eye states. We develop our system by finding the greatest circle—pupil of an eye. So we combine the both Haar classifier and Normalized Summation of Square of Difference template-matching method. We define the eyeball area that is extracted from the eye region as the region of interest (ROI). The ROI helps to differentiate between the open state and closed state of the eyes. The output waveform of the scheme is analogous to binary trend, which alludes the blink detection distinctly. We categorize short, medium, and long blink, depending on the degree of closure and blink duration. Our analysis is operated on medium blink under 15 frames/s. This combined solution for gaze tracking and eye-blink detection system has high detection accuracy and low time consumption. We obtain 98% accuracy at 0° angles for blink detection from both eyes. The system is also extensively experimented with various environments and setups, including variations in illuminations, subjects, gender, angles, processing speed, RAM capacity, and distance. We found that the system performs satisfactorily under varied conditions in real time for both single eye and two eyes detection. These concepts can be exploited in different applications, e.g., to detect drowsiness of a driver, or to operate the computer cursor to develop an eye-operated mouse for disabled people.