Theory of multi-level electoral college for multi-candidte elections and electoral college based face recognition surveillance and intelligent textual information retrival system

多候选人选举的多级选举团理论及基于选举团的人脸识别监控和智能文本信息检索系统

• 批准号: 261403-2006
• 负责人: Chen, Liang
• 金额: $ 1.31万
• 依托单位: University of Northern British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2008
• 资助国家: 加拿大
• 起止时间: 2008-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=394996
• 关键词: Theory; multi; level; electoral; college

On the theoretical front, we will study the stability of the multi-level Electoral College voting system for multi-candidate elections. Its simplest form, the two-level Electoral College, has been used many years in US presidential elections, and applied in areas such as image processing, pattern recognition, information retrieval, and other political elections. Our model will be the first in AI research that illustrates the stability of multi-level Electoral Colleges with respect to region/prefecture size and number of levels. Our theory will guide users of the applications about choosing an adequate number of levels, as well as an adequate region size for each level, when using a multilevel Electoral College to improve the performance of pattern recognition and information retrieval approaches. Developments made on the theoretical front will be applied in two important fields. (1) A face recognition surveillance system will be developed. This system will combine proven active infrared illumination technology for pupil location with a mathematical approach developed in this research program to match unknown faces to known faces on watch lists. The system is expected to be a significant improvement compared to any appearance-based face recognition system in recognition rate vs. false acceptance rate, and being disguise-proof. (2) An intelligent textual information retrieval system will be developed. This system will employ a novel approach, where a document is treated as a physical object which can be described from different "view angles", for document representation so that each document can be divided into blocks and blocks be further divided into subblocks. A vector space model will be employed as the basic strategy for matching a query with each lowest level "block" in a document, and an Electoral College will be employed in higher level blocks. The system is expected to have much better performance in precision vs. recall compared to other approaches. These systems can be further developed for use in national security systems and internet intelligent document retrieval systems respectively.

在理论方面，我们会研究多层次选举团投票制度在多候选人选举中的稳定性。其最简单的形式，两级选举人团，已经在美国总统选举中使用多年，并应用于图像处理，模式识别，信息检索和其他政治选举等领域。我们的模型将是人工智能研究中第一个说明多级选举学院在地区/县规模和级别数量方面的稳定性的模型。我们的理论将指导用户的应用程序选择足够数量的水平，以及适当的区域大小为每个级别，当使用多层次的选举团，以提高性能的模式识别和信息检索方法。理论方面的发展将应用于两个重要领域。(1)将开发人脸识别监控系统。该系统将结合联合收割机证明主动红外照明技术的瞳孔定位与数学方法开发的这项研究计划，以匹配未知的面孔，以已知的面孔观察名单。与任何基于外观的人脸识别系统相比，该系统在识别率与错误接受率方面都有显着改善，并且是防伪装的。(2)将开发一个智能文本信息检索系统。该系统将采用一种新颖的方法，将文件视为一个物理对象，可以从不同的“视角”描述，用于文件表示，以便将每个文件划分为块，并将块进一步划分为子块。将采用向量空间模型作为基本策略，用于将查询与文档中的每个最低级别“块”进行匹配，并在更高级别的块中采用选举团。与其他方法相比，该系统预计在精确度与召回率方面具有更好的性能。这些系统可进一步开发，分别用于国家安全系统和互联网智能文档检索系统。