基于表达残差稀疏性的遮挡人脸识别方法研究

项目名称： 基于表达残差稀疏性的遮挡人脸识别方法研究

项目编号： No.61202276

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 计算机科学学科

项目作者： 米建勋

作者单位： 哈尔滨工业大学

项目金额： 22万元

中文摘要： 人脸识别技术成为近三十年里图像处理和模式识别中最热门的研究主题之一。如何识别被遮挡的人脸是该技术目前面临的一个重大挑战。传统的方法通过提高分类器的鲁棒性来降低遮挡对识别结果的影响，然而效果不够理想且理论基础不够坚实。本项目提出利用线性编码理论来解决遮挡人脸识别问题。我们认为测试样本（带遮挡人脸图片）与用训练样本所表达的测试样本之间的残差满足稀疏性。基于这个性质，我们拟提出三种具有不同特性的新分类方法：第一种方法采用每类样本分别对测试样本进行表达的策略进行快速分类；第二种方法采用共同表达策略来进行更高准确性的分类；第三种方法采用引入非线性函数的策略进行具有更高鲁棒性的分类。由于我们利用了一种全新的理论所构建的方法来解决遮挡人脸识别问题，所以本项目具有一定的理论创新价值。通过本项目的研究，将能进一步推动人脸识别技术的发展，并对我国正大力倡导的信息化社会建设和平安城市建设等起到一定的推动作用。

中文关键词： 人脸识别；模式识别；稀疏表示；生物特征识别；

英文摘要： Face recognition technology has become one of the hottest research topics in image processing and pattern recognition in the latest three decades. Nowadays, we are facing a great challenge on how to make the authentication with occluded face. Conventional methods seek to increase the robustness of classifiers to occlusions. But these means cannot meet the requirement of high accuracy as well as lack of strong theoretical support. This project uses the theory of linear coding to solve the problem of occluded face recognition. We hold that the residual between test samples (with occlusions) and its prediction represented by a combination of the training samples is sparse. Based on such property, we put forward three novel methods with different features. The first one is a rapid classification method which uses training samples of each class to represent a test sample. The second one owns higher recognition accuracy via collaborative representation. The third one has higher robustness by introducing nonlinear function. In our opinion, this project has certain theoretical innovative value, because we introduce a new theory to solve problem of occluded face recognition. We believe that our project can promote the development of face recognition technology and be helpful to the construction of information society an

英文关键词： face recognition；pattern recognition；sparse representation；Biometrics；