An Efficient Robust Eye Localization by Learning the Convolution Distribution Using Eye Template.

Abstract:

:Eye localization is a fundamental process in many facial analyses. In practical use, it is often challenged by illumination, head pose, facial expression, occlusion, and other factors. It remains great difficulty to achieve high accuracy with short prediction time and low training cost at the same time. This paper presents a novel eye localization approach which explores only one-layer convolution map by eye template using a BP network. Results showed that the proposed method is robust to handle many difficult situations. In experiments, accuracy of 98% and 96%, respectively, on the BioID and LFPW test sets could be achieved in 10 fps prediction rate with only 15-minute training cost. In comparison with other robust models, the proposed method could obtain similar best results with greatly reduced training time and high prediction speed.

journal_name

Comput Intell Neurosci

authors

Li X,Dou Y,Niu X,Xu J,Xiao R

doi

10.1155/2015/709072

subject

Has Abstract

pub_date

2015-01-01 00:00:00

pages

709072

eissn

1687-5265

issn

1687-5273

journal_volume

2015

pub_type

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