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

在理论方面，我们将研究多候选人选举的多级选举人团投票系统的稳定性。它最简单的形式是两级选举人团，多年来一直用于美国总统选举，并应用于图像处理、模式识别、信息检索等政治选举领域。我们的模型将是人工智能研究中的第一个，它说明了多级选举人团相对于地区/县的规模和级别数量的稳定性。我们的理论将指导应用程序的用户在使用多级选举人组来提高模式识别和信息检索方法的性能时，如何选择适当的级别数量以及每个级别的适当区域大小。在理论方面取得的进展将应用于两个重要领域。(1)开发人脸识别监控系统。该系统将结合经过验证的主动红外照明技术来定位瞳孔，以及在该研究计划中开发的数学方法，将未知人脸与观察名单上的已知人脸进行匹配。与任何基于外表的人脸识别系统相比，该系统有望在识别率和错误接受率方面有显著提高，并且具有防伪装性。(2)开发智能文本信息检索系统。该系统将采用一种新颖的方法来表示文档，其中文档被视为可以从不同的“视角”描述的物理对象，从而使得每个文档可以被分成块，并且块被进一步分成子块。向量空间模型将被用作将查询与文档中每个最低级别的块进行匹配的基本策略，而较高级别的块中将使用选举人团。与其他方法相比，预计该系统在查准率和查全率方面将有更好的表现。这些系统可以进一步开发，分别用于国家安全系统和互联网智能文献检索系统。

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.