摘要翻译：
提出了一种基于SIFT特征提取的基于图匹配技术的人脸识别系统。虽然SIFT特征已经成功地用于一般的目标检测和识别，但直到最近才被应用于人脸识别。本文进一步研究了基于对旋转、缩放和平移不变的SIFT特征绘制的图匹配拓扑的识别技术的性能。通过考虑空间畸变和局部特征的相似性，利用最大相似度函数将图像上的人脸投影匹配到新的图像上。研究了两种基于图的匹配技术来处理错误对的分配和减少特征数目以找到数据库和查询人脸SIFT特征之间的最优特征集。在BANCA数据库上进行的实验结果证明了该系统在人脸自动识别方面的有效性。
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英文标题：
《Face Identification by SIFT-based Complete Graph Topology》
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作者：
Dakshina Ranjan Kisku, Ajita Rattani, Enrico Grosso, Massimo
  Tistarelli
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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：
  This paper presents a new face identification system based on Graph Matching Technique on SIFT features extracted from face images. Although SIFT features have been successfully used for general object detection and recognition, only recently they were applied to face recognition. This paper further investigates the performance of identification techniques based on Graph matching topology drawn on SIFT features which are invariant to rotation, scaling and translation. Face projections on images, represented by a graph, can be matched onto new images by maximizing a similarity function taking into account spatial distortions and the similarities of the local features. Two graph based matching techniques have been investigated to deal with false pair assignment and reducing the number of features to find the optimal feature set between database and query face SIFT features. The experimental results, performed on the BANCA database, demonstrate the effectiveness of the proposed system for automatic face identification.
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PDF链接：
https://arxiv.org/pdf/1002.0411