Extracting and analysing micro-expressions in a neural network lie detection task

Abstract

O b j e c t i v e s. The objectives of the study are to collect data, develop an algorithm for automatic extraction of microexpressions from video recordings, and form rules for combinations of motor units, based on which basic human emotions are determined.

M e t h o d s. Human facial microexpressions are brief, involuntary reactions that may appear when a person attempts to hide their true emotions. Microexpressions play a key role in lie detection and are an important indicator of the concealment of truthful information. In this article, Action Units (movement units) obtained using the py-feat library from the Facial Action Coding System (FACS) were used to analyse facial expressions.

R e s u l t s. A dataset consisting of video recordings of a group of specific people was collected. Rules were developed based on combinations of action units and their intensities to determine basic emotions. An algorithm for determining and extracting microexpressions from video recordings was also formulated. The results of the algorithm study showed a negative correlation between the emotion of joy and the manifestation of lying.

C o n c l u s i o n. The results obtained allow us to expand the information base for neural network lie detection using a video series of facial images by detecting and analysing microexpressions on them.