Deep Learning-Based Micro Facial Expression Recognition Using an Adaptive Tiefes FCNN Model

Abstract

The scientific community and media have increasingly recognized the significance of micro-expressions as indicators for detecting deception, as they reveal genuine emotions that individuals attempt to conceal. To capitalize on these subtle cues of deceit, researchers have developed applications capable of automatically detecting and recognizing micro-expressions, which are typically imperceptible to the human eye. Facial expressions serve as fundamental ground truth determinants in multimedia applications. Earlier models, such as GA, RFO, X-Boosting, and Gradient Boosting, demonstrate greater efficiency in terms of time and accuracy. However, not all applications are capable of detecting micro facial expressions. In this study, a deep learning-based Tiefes FCNN model is designed specifically for micro facial expression recognition. Implemented using Python software, the proposed model consists of two stages: first, pre-processing is performed using image segmentation, followed by the application of a deep learning model employing Tiefes FCNN technology in the second stage. The experimental results exhibit significant performance improvements, including an accuracy of 99.02%, precision of 98.82%, F1-score of 97.8%, PSNR of 56.31, and CC of 96.31.