Combining Software-Based Eye Tracking and a Wide-Angle Lens for Sneaking Detection

This paper proposes Sneaking Detector, a system which recognizes sneaking on a laptop screen by other people and alerts the owner through several interventions. We utilize a pre-trained deep learning network to estimate eye gaze of sneakers captured by a front-facing camera. Since most of the cameras equipped on laptop computers cannot cover a wide enough range, a commercial wide-angle lens attachment and an image processing are applied in our system. On the dataset involving nine participants following four experiments, it has been realized that our system can estimate the horizontal eye gaze and recognizes whether a sneaker is looking at a screen or not with 78% accuracy.