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Use of Fuzzy Set Classification for Pattern Recognition of the Polygraph

机译：模糊集分类在测谎图形识别中的应用

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This is the final report of a two year study on the use of fuzzy patternrecognition of polygraph data for the identification of truth versus deception. The goals of this study as stated in the original proposal where to: (1) develop a data parsing algorithm which will process polygraph data obtained from the NSA into three domains: time-domain, frequency domain, and correlation domain; (2) design a frizzy classifier algorithm to accept the featurized data and modify its membership functions based on the error between its classification of the polygraph data and the classification in the NSA files; (3) study relationship between number of membership functions an the success of the data classification and; (4) investigate the feasibility of the classification being performed in a near-real-time scenario. The data to be used was MGQT polygraph data. However, the proposal for the second year of the study introduced the goal of comparing the performance of the developed frizzy classification system with 'zone comparison' polygraph data. Ultimately this was changed to be the simulated 'relevant only' data obtained from DODPI. There were two secondary objectives of this project. First, are the features identified as optimal in determining the veracity of a subject optimal for all subjects. Second, are there features not presently being used in polygraph analysis the may be optimal. This report and its attached appendices will show that all objectives of the original proposal where met. A frizzy parser and classifier system were developed that could run in near real-time, achieve performances as good or better than the presently available automatic polygraph systems, and identify new features that previously where not used in polygraph classification. Results of 97% correct for the MGQT data and 100% correct for the 'relevant only' data were achieved.

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