Non-contact Eye-Glance Input Interface Using Video Camera

Abstract

In the past, many studies have been carried out on eyegaze input; however, in this study, we developed an eyeglance input interface that tracks a combination of short eye movements. Unlike eye-gaze input that requires high accuracy measurements, eye-glance input can be detected with only a rough indication of the direction of the eye movements, making it possible to operate even terminals with small screens, such as smartphones. In this study, we used an inexpensive camera to measure eye movements and analyzed its output using the OpenCV, an open source computer vision and machine learning software library, to construct an inexpensive and non-contact interface. In a previous study, we developed an algorithm that detected eye-glance input through image analysis using OpenCV, and fed the result of the algorithm back to our subjects. In that study, the average detection rate for the eye-glance input was 76 %. However, we also observed several problems with the algorithm, particularly the problem of false detections due to blinking of the eyes, and implemented solutions for improvement. In this study, we have made improvement with respect to the unsatisfactory detection rate recorded in our previous study, and addressed problems related to user convenience.

A proposal of eye glance input interface using smartphone built‐in camera

“…Total average discrimination rate of all subjects was 95.6% in fixed mode and 91.3% in gripped mode. Considering that the discrimination rate was about 90% in previous research, 12 one can conclude that gaze input can be realized in smartphones as well as in PCs. In addition, Table 3 gives discrimination rate for every set.…”

“…In previous research, 12 a single evaluation value was obtained through averaging 5% of the largest vectors obtained by optical flow. However, with handheld devices like smartphones, camera shakes more often than not, and the top 5% vectors may include those caused by camera shaking.…”

“…Same as in the previous studies, [9][10][11][12] optical flow of OpenCV was used to measure eye movement. Optical flow is a technique that compares two consecutive images to represent traveling speed of an object in the images with a vector.…”

“…Measurement of glance movement through camerabased image analysis was proposed to solve these problems. Akehi et al [9][10][11][12] acquired face images with an ordinary web camera, and measured glance movement using an open source library (OpenCV). As a result, they obtained waveforms with same features as those measured by EOG.…”