A novel eye status detection method is proposed. Contrary to the most of the previous methods, this new method is not based on an explicit eye appearance model. Instead, the detection is based on a deep learning methodology, where the discriminant function is learned from a large set of exemplar images of eyes at different state, appearance, and 3D position. The technique is based on the Convolutional Neural Network (CNN) architecture. To assess the performance of the proposed method, it has been tested against two techniques, namely: SVM with SURF Bag of Features and Adaboost with HOG and LBP features. It has been shown that the proposed method outperforms these with a considerable margin on a two-class problem, with the two classes defined as "opened" and "closed". Subsequently the CNN architecture was further optimised on a three-class problem with "opened", "closed", and "partially-opened" classes. It has been demonstrated that it is possible to implement a real-time eye status detection working with a large variability of head poses, appearances and illumination conditions. Additionally, it has been shown that an eye blinking estimation based on the proposed technique is at least comparable with the current state-of-the-art on standard eye blinking datasets.
REF just added a third level (partially opened) to the two eye states (opened and closed).
Online Eye Status Detection in the Wild with Convolutional Neural Networks
VISIGRAPP
Computer Science