NDLI: A hybrid classifier and template matching method for eye detecting in real-time video

Content Provider	IEEE Xplore Digital Library
Author	Ying Chen Chunping Hou Kaining Lu Yuan Zhou Mei Song
Copyright Year	2015
Description	Author affiliation: Sch. of Electr. Inf. Eng., Tianjin Univ., Tianjin, China (Ying Chen; Chunping Hou; Kaining Lu; Yuan Zhou) \|\| Sch. of Electr. Inf. Eng., Beijing Univ. of Posts & Telecommun., Beijing, China (Mei Song)
Abstract	Eye detecting is an important part of Computer Vision. Even if there are a large number of the methods for eye-detecting nowadays, most of them are vulnerable to different light conditions and complex environments. This paper proposed a novel Classifier and Template Matching method for eye detecting in real-time video. The presented method includes three parts. Firstly, read the video from the camera frame by frame, we utilize the existing strong and efficient classifier which is produced by Harr-like features to find out the human face in each frame. Secondly, shrink the position of eyes based on our experience in the detected face we have got. Thirdly, get the exact position of eyes with template matching algorithm. Experiment results demonstrate that the method is robust for detecting human eyes in most situations.
Starting Page	171
Ending Page	174
File Size	242714
Page Count	4
File Format	PDF
ISBN	9781479965984
e-ISBN	9781479966004
DOI	10.1109/ICCSE.2015.7250237
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-22
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Computers Computer vision Streaming media Template-matching Real-time systems Eye-detection Classification algorithms Classifier Face Adaboost
Content Type	Text
Resource Type	Article

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