Real time eye detector with cascaded convolutional neural networks

Bin LI, Hong FU

Research output: Contribution to journalArticlespeer-review

38 Citations (Scopus)


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. Copyright © 2018 Bin Li and Hong Fu.
Original languageEnglish
Article number1439312
JournalApplied Computational Intelligence and Soft Computing
Early online date22 Apr 2018
Publication statusPublished - 2018


Li, B., & Fu, H. (2018). Real time eye detector with cascaded convolutional neural networks. Applied Computational Intelligence and Soft Computing, 2018. Retrieved from


Dive into the research topics of 'Real time eye detector with cascaded convolutional neural networks'. Together they form a unique fingerprint.