基于零交叉编码图像投影的鲁棒眼中心定位
Robust Eye Centers Localization with Zero--Crossing Encoded Image Projections
摘要：
本文提出了一种新的框架，通过联合使用归一化图像投影编码和多层感知器（MLP）分类器的眼睛中心定位。编码是新颖的，它包括在识别零交叉和提取相关的参数，从所产生的模式。压缩的标准化投影产生特征描述符，该特征描述符是适当训练的MLP的输入，用于在各种类别的图像区域之间进行区分。所提出的框架形成了一个快速和可靠的系统眼中心定位，特别是在无约束环境中的人脸表情分析的背景下。我们成功地测试了所提出的方法在各种各样的数据库，包括BioID，科恩-Kanade，扩展耶鲁B和标记的面孔在野生（LFW）数据库。
英文摘要：
This paper proposes a new framework for the eye centers localization by the joint use of encoding of normalized image projections and a Multi Layer Perceptron (MLP) classifier. The encoding is novel and it consists in identifying the zero-crossings and extracting the relevant parameters from the resulting modes. The compressed normalized projections produce feature descriptors that are inputs to a properly-trained MLP, for discriminating among various categories of image regions. The proposed framework forms a fast and reliable system for the eye centers localization, especially in the context of face expression analysis in unconstrained environments. We successfully test the proposed method on a wide variety of databases including BioID, Cohn-Kanade, Extended Yale B and Labelled Faces in the Wild (LFW) databases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
