Efficient Eye-Blinking Detection on Smartphones: A Hybrid Approach Based on Deep Learning

Abstract EN

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.