Efficient Deception Detection Using Multimodal Facial and Audio Transcript Features

This paper presents an efficient deception detection framework leveraging multi-modal facial and audio transcript features, achieving a notable 91.2% accuracy using a Modified Convolution Neural Network (Mod CNN) model. Deception detection, a critical challenge in security, forensic, and psychological analysis, benefits significantly from the integration of multi-modal data that captures subtle cu…