The influence of micro-expressions on deception detection

Abstract Facial micro-expressions are universal symbols of emotions that provide cohesion to interpersonal communication. At the same time, the changes in micro-expressions are considered to be the most important hints in the psychology of emotion. Furthermore, analysis and recognition of these micro-expressions have pervaded in various areas such as security and psychology. In security-related matters, micro-expressions are widely used to detect deception. In this research, a deep learning model that interprets the changes in the face into meaningful information has been trained using The Facial Expression Recognition 2013 dataset. Necessary data is also obtained through live stream or video stream by detecting via computer vision and evaluating with the trained model. Finally, the data obtained is transformed into graphic and interpreted to determine whether the people are trying to deceive or not. The deception classification accuracy of the custom trained model is 74.17% and the detection of the face with high precision using the computer vision methods increased the accuracy of the obtained data and provided it to be interpreted correctly. In this respect, the study differs from other studies using the same dataset. In addition, it is aimed to facilitate the deception detection which is performed in a complex and expensive way, by making it simple and understandable.