Authors:
Essa R. Anas
;
Pedro Henriquez
and
Bogdan J. Matuszewski
Affiliation:
University of Central Lancashire, United Kingdom
Keyword(s):
Convolutional Neural Network CNN, Deep Learning, Eye Status Detection, Eye Blinking Estimation.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Medical Image Applications
Abstract:
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 d
etection 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.
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