Automatic credibility assessment screening using discriminate analysis of skin conductance response and photoplethysmograph signals

Credibility assessment screening by a small system and receiving optimum result in minimum time is a basic need in critical gates. Therefore the aim of this research is automatic detection of stress in guilty persons through skin conductance response and photoplethysmograph signals which are convenient and ease-of-use sensors .In this paper, a set of database with interview protocol (including control and relevant questions) in mock crime (Stealing jewels) is provided. 40 subjects participated in the experiments. 28 time-frequency features are extracted from two mentioned signals. The function of dimension reduction algorithms including principal component analysis, Kernel based PCA, linear discriminant analysis, cluster based LDA is evaluated to select optimum features. Support Vector Machine, Bayesian and AdaBoost are used as classifiers. The evaluation of algorithms on database is based on LOO method. Maximum accuracy (81.08%) is obtained through principal components analysis as feature selection method and Bayesian as classifier.