Open AccessTiny and Blurred Face Alignment for Long Distance Face Recognition
Author(s) -
Ban KyuDae,
Lee Jaeyeon,
Kim DoHyung,
Kim Jaehong,
Chung Yun Koo
Publication year - 2011
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.11.1510.0022
Subject(s) - artificial intelligence , computer vision , adaboost , computer science , facial recognition system , face (sociological concept) , three dimensional face recognition , face detection , object class detection , feature (linguistics) , magnification , pattern recognition (psychology) , support vector machine , social science , linguistics , philosophy , sociology
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.