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Applied Computational Intelligence and Soft Computing
Volume 2018, Article ID 1439312, 8 pages
https://doi.org/10.1155/2018/1439312
Research Article

Real Time Eye Detector with Cascaded Convolutional Neural Networks

Bin Li1,2,3 and Hong Fu3

1Xi’an Institute of Optics and Precision Mechanics of CAS, Xi’an 710119, China
2University of Chinese Academy of Sciences, Beijing 100049, China
3Chu Hai College of Higher Education, Tuen Mun, Hong Kong

Correspondence should be addressed to Hong Fu; kh.ude.iahuhc@ufgnoh

Received 12 January 2018; Revised 12 March 2018; Accepted 14 March 2018; Published 22 April 2018

Academic Editor: Erich Peter Klement

Copyright © 2018 Bin Li and Hong Fu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

An accurate and efficient eye detector is essential for many computer vision applications. In this paper, we present an efficient method to evaluate the eye location from facial images. First, a group of candidate regions with regional extreme points is quickly proposed; then, a set of convolution neural networks (CNNs) is adopted to determine the most likely eye region and classify the region as left or right eye; finally, the center of the eye is located with other CNNs. In the experiments using GI4E, BioID, and our datasets, our method attained a detection accuracy which is comparable to existing state-of-the-art methods; meanwhile, our method was faster and adaptable to variations of the images, including external light changes, facial occlusion, and changes in image modality.