Abstract
We propose an efficient method that can be used for eye-blinking detection or eye tracking on smartphone platforms in this paper. Eye-blinking detection or eye-tracking algorithms have various applications in mobile environments, for example, a countermeasure against spoofing in face recognition systems. In resource limited smartphone environments, one of the key issues of the eye-blinking detection problem is its computational efficiency. To tackle the problem, we take a hybrid approach combining two machine learning techniques: SVM (support vector machine) and CNN (convolutional neural network) such that the eye-blinking detection can be performed efficiently and reliably on resource-limited smartphones. Experimental results on commodity smartphones show that our approach achieves a precision of 94.4% and a processing rate of 22 frames per second.
Highlights
Eye-tracking and/or -blinking detection algorithms have various applications in smartphone platforms
Deep convolutional neural network (CNN) models have been successfully applied to solve various computer vision problems in a past few years, running deep CNN models on smartphones are still considered to be challenging due to the computational complexity of deep CNN models. When it comes to eye-blinking detection, the applicability of the CNN technique becomes more restricted since the eye-blinking detection problem imposes a stricter real-time requirement, that is, a processing rate over 10 frames per second, than eye tracking and must be Mobile Information Systems more computationally efficient
We propose a new region of interest (ROI) selection technique that suits to mobile environments
Summary
Eye-tracking and/or -blinking detection algorithms have various applications in smartphone platforms It can be used as a countermeasure against spoofing in face recognition systems [1]. Deep CNN models have been successfully applied to solve various computer vision problems in a past few years, running deep CNN models on smartphones are still considered to be challenging due to the computational complexity of deep CNN models When it comes to eye-blinking detection, the applicability of the CNN technique becomes more restricted since the eye-blinking detection problem imposes a stricter real-time requirement, that is, a processing rate over 10 frames per second (fps), than eye tracking and must be Mobile Information Systems more computationally efficient.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
