Head Mounted Pupil Tracking Using Convolutional Neural Network

TL;DR

This paper presents a CNN-based algorithm for head mounted pupil detection that combines multiple features and quality evaluation to improve accuracy under challenging conditions.

Contribution

It introduces a novel CNN approach that integrates multiple pupil features and quality assessment for robust head mounted pupil tracking.

Findings

Outperforms current state-of-the-art methods

Effective under noisy and variable illumination conditions

Demonstrates high precision in head mounted tracking scenarios

Abstract

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.