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

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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.