NDLI: A robust eye localization method for low quality face images

Content Provider	IEEE Xplore Digital Library
Author	Dong Yi Zhen Lei Li, S.Z.
Copyright Year	2011
Description	Author affiliation: Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China (Dong Yi; Zhen Lei; Li, S.Z.)
Abstract	Eye localization is an important part in face recognition system, because its precision closely affects the performance of face recognition. Although various methods have already achieved high precision on the face images with high quality, their precision will drop on low quality images. In this paper, we propose a robust eye localization method for low quality face images to improve the eye detection rate and localization precision. First, we propose a probabilistic cascade (P-Cascade) framework, in which we reformulate the traditional cascade classifier in a probabilistic way. The P-Cascade can give chance to each image patch contributing to the final result, regardless the patch is accepted or rejected by the cascade. Second, we propose two extensions to further improve the robustness and precision in the P-Cascade framework. There are: (1) extending feature set, and (2) stacking two classifiers in multiple scales. Extensive experiments on JAFFE, BioID, LFW and a self-collected video surveillance database show that our method is comparable to state-of-the-art methods on high quality images and can work well on low quality images. This work supplies a solid base for face recognition applications under unconstrained or surveillance environments.
Starting Page	1
Ending Page	6
File Size	890699
Page Count	6
File Format	PDF
ISBN	9781457713583
e-ISBN	9781457713590
e-ISBN	9781457713576
DOI	10.1109/IJCB.2011.6117499
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational efficiency
Content Type	Text
Resource Type	Article

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