NDLI: Face image registration methods using Normalized Cross Correlation

Content Provider	IEEE Xplore Digital Library
Author	Kyu-Dae Ban Jaeyeon Lee Dae Hwan Hwang Yun-Koo Chung
Copyright Year	2008
Description	Author affiliation: U-Robot Res. Div., ETRI, Daejeon (Jaeyeon Lee; Dae Hwan Hwang; Yun-Koo Chung) \|\| Dept. of Comput. Software & Eng., UST, Daejeon (Kyu-Dae Ban)
Abstract	In the many processing stage of face recognition, one of the most important parts is face detection. In the recent years, there has been much progress in face detection. Many face recognition system adopt or develop the Adaboost: Viola-Jones face detection system. But the detected face image by adaboost method has serious problems. Face regions are apt to be different at each time, and the detected face image includes the rotated face. These issues give the bad effect to the feature extraction stage of the face recognition. In order to solve these problems, many researchers normalize the scale of face through the detection of detail element of a face, especially the eye detection. Generally, the home service robot mounts the low resolution camera. As the distance between a robot and a user becomes increasing, it is very difficult to obtain the good result by those methods. In this paper, Normalized Cross Correlation is used to detect the exact face region in the low resolution face image. The experiments showed that our method using NCC give the much better face recognition rate.
Starting Page	2408
Ending Page	2411
File Size	585001
Page Count	4
File Format	PDF
ISBN	9788995003893
DOI	10.1109/ICCAS.2008.4694210
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-14
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	ICROS
Subject Keyword	Image registration Image resolution Service robots Face recognition Robot vision systems Lighting Control systems Cameras Face detection Normalized Cross Correlation Intelligent service robot Intelligent robots
Content Type	Text
Resource Type	Article

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