A Comprehensive Review On Various State Of Art Techniques For Eye Blink Detection

27 Nov 2019Sannidhan MSSunil Kumar AithalAbhir Bhandary

Computer Vision is considered to be one of the most important areas in research and has focused on developing many applications that has proved to be useful for both research and societal benefits. Today we have been witnessing many of the road mishaps happening just because of the lack of concentration while driving.As a part of avoiding this kind of disaster happening in day to day life there are many technologies focusing on keeping track of the vehicle drivers concentration.One such technology uses the method of eye blink detection to find out the concentration level of the driver.With the advent of many high end camera devices with cost effectiveness factor today it has become more efficient and cheaper to use eye blink detection for keeping track of the concentration level of the driver.Hence this paper presents an exhaustive review on the implementations of various eye blink detection algorithms.The detection system has also extended its application in various other fields like drowsiness detection and fatigue detection and expression detection...

PDF Abstract


No code implementations yet. Submit your code now


Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet