A robust eye localization method for low quality face images

机译：一种用于低质量人脸图像的鲁棒的眼睛定位方法

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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.

机译：眼睛定位是人脸识别系统中的重要组成部分，因为它的精确度会密切影响人脸识别的性能。尽管各种方法已经在高质量的面部图像上实现了高精度，但是它们的精度将在低质量的图像上下降。在本文中，我们提出了一种针对低质量人脸图像的鲁棒的眼睛定位方法，以提高眼睛的检测率和定位精度。首先，我们提出了一个概率级联（P-Cascade）框架，在该框架中我们以概率方式重新构造了传统的级联分类器。无论级联接受还是拒绝补丁，P-Cascade都可以为每个图像补丁贡献最终结果。其次，我们提出了两个扩展，以进一步提高P-Cascade框架的鲁棒性和精度。有：（1）扩展要素集，和（2）以多个比例堆叠两个分类器。在JAFFE，BioID，LFW和自我收集的视频监控数据库上进行的大量实验表明，我们的方法可与高质量图像上的最新方法相媲美，并且可以在低质量图像上很好地工作。这项工作为在不受约束或监视的环境下的人脸识别应用程序提供了坚实的基础。

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《2011 International Joint Conference on Biometrics》|2011年|p.1-6|共6页
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Dong Yi; Zhen Lei; Li Stan Z.;
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中图分类生物工程学（生物技术）;
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A robust eye localization method for low quality face images

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