The main objective of this project is to identify the eye blink in real-time and therefore drowsiness state of the driver can be analysed so that it generates an alert immediately to wake up the driver, hence we can able to prevent road accidents.
Effective and real-time eyeblink detection is of wide-range applications, such as deception detection, drive fatigue detection, face anti-spoofing. Despite previous efforts, most of existing focus on addressing the eyeblink detection problem under constrained indoor conditions with relative consistent subject and environment setup. Nevertheless, towards practical applications, eyeblink detection in the wild is highly preferred, and of greater challenges.
Keywords: Eyeblink detection, eyeblink in the wild, spatial-temporal pattern recognition, LSTM, appearance and motion.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Hardware requirements:
Software requirements