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摘要翻译：
脸是高度可变形的物体，很容易随着时间的推移而改变其外观。并不是所有的面部区域都具有相同的可变性。因此，从人脸的独立区域解耦信息对于提高任何人脸识别技术的鲁棒性都是至关重要的。提出了一种基于独立人脸区域相关SIFT特征提取与匹配的鲁棒人脸识别技术。提出了全局匹配和局部匹配（作为从零件识别）的策略。局部策略是基于匹配与面部地标（如眼睛和嘴巴）相关的单个显著面部筛选特征。对于全局匹配策略，将所有SIFT特征组合在一起形成单个特征。为了减小识别误差，采用Dempster-Shafer决策理论对两种匹配技术进行融合。用ORL和IITK人脸数据库对所提出的算法进行了评价。实验结果证明了本文提出的人脸识别技术在部分遮挡或信息缺失情况下的有效性和潜力。
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英文标题：
《Face Recognition by Fusion of Local and Global Matching Scores using DS
Theory: An Evaluation with Uni-classifier and Multi-classifier Paradigm》
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作者：
Dakshina Ranjan Kisku, Massimo Tistarelli, Jamuna Kanta Sing, Phalguni
Gupta
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最新提交年份：
2010
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Computer Vision and Pattern Recognition 计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
Faces are highly deformable objects which may easily change their appearance over time. Not all face areas are subject to the same variability. Therefore decoupling the information from independent areas of the face is of paramount importance to improve the robustness of any face recognition technique. This paper presents a robust face recognition technique based on the extraction and matching of SIFT features related to independent face areas. Both a global and local (as recognition from parts) matching strategy is proposed. The local strategy is based on matching individual salient facial SIFT features as connected to facial landmarks such as the eyes and the mouth. As for the global matching strategy, all SIFT features are combined together to form a single feature. In order to reduce the identification errors, the Dempster-Shafer decision theory is applied to fuse the two matching techniques. The proposed algorithms are evaluated with the ORL and the IITK face databases. The experimental results demonstrate the effectiveness and potential of the proposed face recognition techniques also in the case of partially occluded faces or with missing information.
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PDF链接：
https://arxiv.org/pdf/1002.0382
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