Towards Real-Time Emotion Recognition in Fog Computing-Based Systems: Leveraging Interpretable PCA_CNN, YOLO with Self-Attention Mechanism

Nora El Rashidy^1*Eman Allogmani²Esraa Hassan¹Hela Elmannai³Zainab H. Ali¹

• ¹Kafrelsheikh University, Kafr el-Sheikh, Egypt
• ²Majmaah University College of Engineering, Al Majmaah, Saudi Arabia
• ³Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

ABSTRACT Emotion estimation from face expression analysis has been extensively examined in computer science. In contrast, classifying expressions depends on appropriate facial features and their dynamics. Despite the promising accuracy results in handled and favorable conditions, processing faces acquired at a distance, entailing low-quality images, still needs an influential performance reduction. The primary objective of this study is to introduce a Real-Time Emotion Recognition system-based Fog Technique, which was developed to track and observe human emotional states in real time.This paper provides a comprehensive integration of PCA-based feature selection with a specific version of YOLO (YOLOv8), in addition to spatial attention for real-time recognition. The developed system demonstrates superiority in edge deployment capabilities compared to existing approaches. The proposed model is compared with the EL Rashidy et al. CNN_PCA hybrid model. First, Principal Component Analysis (PCA) is employed as a dimension-reduction tool, focusing on the most informative characteristics during training, and then CNN as classification layer. The proposed system's performance is assessed via a dataset of 35,888 facial photos classified into seven classes: anger, fear, happiness, neutral, sadness, surprise, and disgust. The constructed model surpasses established pre-trained models, such as VGG, ResNet, and MobileNet, with different evaluation metrics. First, the PCA_CNN model achieved superior accuracy, precision, recall, and Area Under the Curve (AUC) scores of 0.936, 0.971, 0.843, 0.871, and 0.943.YOLO v8 aith attention model achieved 0.986, 0.902, 0.941, and 0.952. Additionally, the model exhibits significantly faster processing time, completing computations in just 610 seconds than other pre-trained models. To validate the model's superiority, extensive testing on additional datasets consistently yields promising performance results, further validating the efficiency and effectiveness of our developed model in real-time emotion recognition for advancing affective computing applications.