Efficient Eye-Blinking Detection on Smartphones: A Hybrid Approach Based on Deep Learning
- Authors
- Han, Young-Joo; Kim, Wooseong; Park, Joon-Sang
- Issue Date
- 2018
- Publisher
- HINDAWI LTD
- Citation
- MOBILE INFORMATION SYSTEMS, v.2018
- Journal Title
- MOBILE INFORMATION SYSTEMS
- Volume
- 2018
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/4787
- DOI
- 10.1155/2018/6929762
- ISSN
- 1574-017X
- 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.
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