Abstract
Currently, researchers are actively working on methods for making decisions based on the results of automated analysis of the sequence of images received from video cameras. The purpose of our work is to develop a probabilistic approach to predicting human behavior based on modeling the pupillary response. The novelty of the research lies in the use of the parameters of the pupillogram obtained in the process of exposure of a person to a stimulus material as a prognostic tool. The likelihood was assessed by empirical frequencies (stimulus material puts a person in a tense state). In total, 40 people took part in the experiments to identify the stress state, including 20 males and 20 females. The average age of the participants was 20 years. All participants in the experiment were volunteers. During the experiment, we measured the change in galvanic skin response (GSR) and pupillary response. The GSR was measured using the "Aktivatiometer 6" hardware and software complex. Pupilary response was monitored using a pupillographic module for registering changes in pupil size. Pupillographic module consists of a special helmet and a digital video camera ZWO ASI120MC. The camcorder has the following main technical characteristics: frame rate - 30 fps, lens with optical zoom 1X-100X, resolution - not less than 600 TVL, signal-to-noise ratio not worse than SNR=40 dB, focusing range - from 20 mm to infinity. The captured video files were dissected and analyzed using the ImageJ software. Preliminary calibration is individual in nature, allows you to take into account the initial psychophysical state of a person. The assessment of the most probable S \ Smed value for calibration stimuli that does not induce a stress state falls within the interval (1± 0,2). A strong correlation was found between GSR and the duration of the increase in pupil size (p=0,7). On the basis of pupillary response modeling, a decision-making algorithm for security systems is proposed.
Published Version
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