Intentional-Deception Detection Based on Facial Muscle Movements in an Interactive Social Context

Abstract

Micro-expression, which is generated by facial muscle movements, could be a crucial cue for deception detection. In the existed research investigating the relationship between facial muscles and deception detection, researchers have focused almost exclusively on two muscles, i.e., zygomaticus and corrugator supercilii, based on the theoretical basis that they are highly associated with positive and negative expressions. However, the aim of this study is to demonstrate the direct relationship between facial muscle movements and deception detection. Addressing this issue, this paper proposes an experimental paradigm with high ecological validity that uses electromyography (EMG) signals to precisely examine the role of facial muscle movements in deception detection. Moreover, we propose a vector-based sequential forward selection (VSFS) algorithm to identify the muscle (or muscle combination) most closely associated with lying. Based on our proposed approach, the importance of seven selected facial muscles was explored by comparing the corresponding facial EMG (fEMG) between truth and lying conditions. First, the present study found that the zygomaticus and corrugator supercilii could play important roles in deception detection, and our findings are consistent with existed research. Second, the experiment result verified that the muscles related to deception detection were consistent with those with higher frequency occurring in micro-expression. Moreover, the present study provides a theoretical basis that intelligent micro-expressions analysis could improve the lie detection performance by focusing on the area of the forehead, eyebrows, and cheeks.