Deception detection based on micro-expression and feature selection methods

Video-based deception detection, which identifies lies through facial expressions and behaviors, has proven to be an effective approach in criminal interrogation. In this paper, a deception detection framework is proposed that incorporates a novel set of features and a unique deception detection method based on facial expressions, particularly micro-expressions. Two feature selection methods are applied to optimize these features. Specifically, facial action units (AUs), eye gaze, and head pose were extracted using the OpenFace toolkit, while micro-expression information was obtained via the SOFTNet model, trained on the CAS(ME) $$^{2}$$ data set. A sequential combination of the Fischer Score and Principal Component Analysis (PCA) was employed for feature selection, with a Support Vector Machine (SVM) used for classification. Feature importance analysis indicated that micro-expression (ME) information had a significant impact on the deception detection task. The proposed framework was evaluated on two publicly available data sets, achieving accuracies of 98.07% and 98.23% on the real-life and MU3D data sets, respectively, thus demonstrating its superiority over prior approaches in the literature.