MAC-Gaze uses IMU-based motion detection and replay-based continual learning to automatically recalibrate mobile gaze trackers, reducing gaze estimation error by 19.9% on RGBDGaze and 31.7% on MotionGaze.
GazeSwipe: Enhancing Mobile Touchscreen Reachability through Seamless Gaze and Finger-Swipe Integration
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abstract
Smartphones with large screens provide users with increased display and interaction space but pose challenges in reaching certain areas with the thumb when using the device with one hand. To address this, we introduce GazeSwipe, a multimodal interaction technique that combines eye gaze with finger-swipe gestures, enabling intuitive and low-friction reach on mobile touchscreens. Specifically, we design a gaze estimation method that eliminates the need for explicit gaze calibration. Our approach also avoids the use of additional eye-tracking hardware by leveraging the smartphone's built-in front-facing camera. Considering the potential decrease in gaze accuracy without dedicated eye trackers, we use finger-swipe gestures to compensate for any inaccuracies in gaze estimation. Additionally, we introduce a user-unaware auto-calibration method that improves gaze accuracy during interaction. Through extensive experiments on smartphones and tablets, we compare our technique with various methods for touchscreen reachability and evaluate the performance of our auto-calibration strategy. The results demonstrate that our method achieves high success rates and is preferred by users. The findings also validate the effectiveness of the auto-calibration strategy.
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MAC-Gaze: Motion-Aware Continual Calibration for Mobile Gaze Tracking
MAC-Gaze uses IMU-based motion detection and replay-based continual learning to automatically recalibrate mobile gaze trackers, reducing gaze estimation error by 19.9% on RGBDGaze and 31.7% on MotionGaze.