  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.
