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 Neuroscijournal_title
Computational intelligence and neuroscienceauthors
Li X,Dou Y,Niu X,Xu J,Xiao Rdoi
10.1155/2015/709072subject
Has Abstractpub_date
2015-01-01 00:00:00pages
709072eissn
1687-5265issn
1687-5273journal_volume
2015pub_type
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