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http://dx.doi.org/10.4218/etrij.11.1510.0022

Tiny and Blurred Face Alignment for Long Distance Face Recognition  

Ban, Kyu-Dae (IT Convergence Technology Research Laboratory, ETRI, Department of Computer Software & Engineering, University of Science and Technology)
Lee, Jae-Yeon (IT Convergence Technology Research Laboratory, ETRI)
Kim, Do-Hyung (IT Convergence Technology Research Laboratory, ETRI)
Kim, Jae-Hong (IT Convergence Technology Research Laboratory, ETRI)
Chung, Yun-Koo (IT Convergence Technology Research Laboratory, ETRI, Department of Computer Software & Engineering, University of Science and Technology)
Publication Information
ETRI Journal / v.33, no.2, 2011 , pp. 251-258 More about this Journal
Abstract
Applying face alignment after face detection exerts a heavy influence on face recognition. Many researchers have recently investigated face alignment using databases collected from images taken at close distances and with low magnification. However, in the cases of home-service robots, captured images generally are of low resolution and low quality. Therefore, previous face alignment research, such as eye detection, is not appropriate for robot environments. The main purpose of this paper is to provide a new and effective approach in the alignment of small and blurred faces. We propose a face alignment method using the confidence value of Real-AdaBoost with a modified census transform feature. We also evaluate the face recognition system to compare the proposed face alignment module with those of other systems. Experimental results show that the proposed method has a high recognition rate, higher than face alignment methods using a manually-marked eye position.
Keywords
Face alignment; face detection; face recognition; human robot interaction;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
Times Cited By Web Of Science : 0  (Related Records In Web of Science)
Times Cited By SCOPUS : 5
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