Classification of Intentional Eye-blinks using Integration Values of Eye-blink Waveform

We propose a method to automatically classify eye-blink types using the eye-blink waveform integral value. The method is assumed to apply to an input interface using eye and It performs automatic detection of intentional blinks. Attempts to treat eye gestures and blinks as input channels in addition to conventional gaze input has studied due to the spread of gaze tracking and gaze input interfaces recently. However, classifying the eye-blink type as intentional or spontaneous using existing eye-blink classification methods is difficult because eye-blinks are highly individual motions that are significantly influenced by various conditions. Therefore, in this research, we construct a more robust measurement environment, which does not require a strict setting such as fixing the relative distance between the face and the camera even for non-contact measurement. In order to realize this, we defined new feature parameters are defined to correct the individual differences from moving image measuring by Web camera to assume applying on mobile interface. The proposed method performs automatic detection of intentional blinks by automatically determining the threshold of blink types based on the waveform integration value as new feature parameter. We also constructed a blink measurement system to evaluate the proposed method and evaluated the proposed method by experiment. The system splits the interlaced image field into disparate fields for blink measurement with sufficient temporal resolution. It then extracts the waveform feature parameters and automatically classifies the eye-blink types. Experimental results show successful classification of intentional eye-blinks with 86% average accuracy, thus demonstrated the high accuracy of the proposed method compared to conventional methods based on eye-blink duration.