Exploring the Accuracy of Artificial Intelligence in Detecting Lies Through Micro-expression Analysis

Micro-expressions are the subtle and rapid movements of human facial expressions that could reveal a person’s true emotions, including emotions that people attempt to suppress, hide, or restrain. Many recent papers have researched facial expression recognition systems in video sequences using GRU models. However, they haven’t found a good relevance of micro-expression (ME) in detecting deceptive behaviors. In order to improve and contribute to the development of the system, we propose a micro-expression lie detection system with GRU’s hyperparameter optimization and explore its accuracy. FER-2013 is used for expression recognition learning and the dataset containing video clips of courtroom trials is used for deception detection learning. Several normalization techniques are done in the process. The CNN model with eight convolutional layers and three fully linked layers is used to train the facial expression recognition system. To improve its accuracy, multiple GRU parameter settings are employed. We used the model on the test dataset after training it. The outcome shows that it was 92.31% accurate. The confusion matrix predicts 12 out of 13 outcomes, with 100% accuracy on the deceptive class and 85% accuracy on the truthful class.