Eye Blink Detection

Eye blink detection has many uses, the most common are human computer interaction for disabled people, dry eye monitoring systems, and fatigue detection. We analyze the state-of-the-art methods with emphasis on usability. We focus on real-time methods working in the real-world environment and using a common webcam. We introduce two new datasets which are the biggest datasets available. The proposed annotation contains face and eye corners positions, so the eye blink detection performance is not influenced either by face or eye detection methods. An evaluation procedure defines True positives with intersection over union metric. Two state-of-the-art methods are introduced. The first method analysis motion vectors using an average motion vector with standard deviation. These are the input to the carefully designed state machine. With the second method, we evaluate different features from the related work as the input to a Recurrent Neural Network (RNN). This method achieves the best results on the biggest and the most challenging dataset