Head Mounted Pupil Tracking Using Convolutional Neural Network
This work addresses the problem of precise pupil tracking for applications like human-computer interaction or medical diagnostics, but it is incremental as it builds on existing feature-based methods with a CNN for quality evaluation.
The paper tackles head-mounted pupil tracking by proposing a CNN-based algorithm that combines three pupil features to improve detection precision under noisy conditions, achieving better performance than the state-of-the-art.
Pupil tracking is an important branch of object tracking which require high precision. We investigate head mounted pupil tracking which is often more convenient and precise than remote pupil tracking, but also more challenging. When pupil tracking suffers from noise like bad illumination, detection precision dramatically decreases. Due to the appearance of head mounted recording device and public benchmark image datasets, head mounted tracking algorithms have become easier to design and evaluate. In this paper, we propose a robust head mounted pupil detection algorithm which uses a Convolutional Neural Network (CNN) to combine different features of pupil. Here we consider three features of pupil. Firstly, we use three pupil feature-based algorithms to find pupil center independently. Secondly, we use a CNN to evaluate the quality of each result. Finally, we select the best result as output. The experimental results show that our proposed algorithm performs better than the present state-of-art.