2D Cascaded AdaBoost for Eye Localization

Authors:
Zhiheng Niu;Shiguang Shan;Shengye Yan;Xilin Chen;Wen Gao
Affiliations:
Harbin Institute of Technology, Harbin, China;ICT-ISVISION FRJDL, Institute of Computing Technology, CAS, Beijing, China;ICT-ISVISION FRJDL, Institute of Computing Technology, CAS, Beijing, China;Harbin Institute of Technology, Harbin, China;Harbin Institute of Technology, Harbin, China
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Year:
2006

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Abstract

In this paper, 2D cascaded AdaBoost, a novel classifier designing framework, is presented and applied to eye localization. By the term "2D, we mean that in our method there are two cascade classifiers in two directions: The first one is a cascade designed by bootstrapping the positive samples, and the second one, as the component classifiers of the first one, is cascaded by bootstrapping the negative samples (please refer to Fig.1). The advantages of the 2D structure include: (1) It greatly facilitates the classifier designing on huge-scale training set; (2) It can easily deal with the significant variations within the positive (or negative) samples; (3) Both the training and testing procedures are more efficient. The proposed structure is applied to eye localization and evaluated on four public face databases, extensive experimental results verified the effectiveness, efficiency, and robustness of the proposed method.