MULTI-USER EMOTION RECOGNITION IN CROWDED SCENES VIA ENHANCED PARTICLE SWARM OPTIMIZED RECURRENT NEURAL NETWORKS

Psychological group level emotion recognition (GER) is significant because it facilitates understanding and identifying the behavior of people in large congregations, organizations, and other facilities that require surveillance. Some issues include occlusions, dynamic facial expressions, variation in pose, and even low-resolution faces. A new framework of combining the EPSO (Enhanced Particle Swarm Optimization) algorithm for identifying significant features to tackle these challenges and using RNN to learn the sequences involved. The two-fold process involves feature reduction followed by meaningful group emotion classification, taking into consideration the temporal dynamics of emotions. The Acted Facial Expressions in the Wild (AFEW) dataset is used to validate the proposed EPSO-RNN model by comparing it with the baseline methods, such as CNN, SVM, and VGG-16. The experimental findings reveal better EPSO-RNN results in different measures of recognizing group-level emotions.  In order to enhance the capabilities of the existing PSO, the proposed EPSO-RNN framework finally combines the enhancement of the feature space optimization and sequential emotion modeling by using recurrent neural network structures. Unlike existing methods that primarily rely on deep convolutional architectures or conventional classifiers with limited adaptability to real-world group settings, this study fills a significant gap by introducing a hybrid EPSO-RNN model that jointly optimizes feature selection and temporal emotion learning. The novelty lies in leveraging enhanced particle swarm optimization to distill high-impact features from noisy group environments, followed by RNN-driven modeling of emotional evolution over time. Experimental validation on the AFEW dataset shows a substantial improvement in classification accuracy and F1-score, outperforming traditional CNN, SVM, and VGG-16 baselines. These findings confirm the framework's potential for robust and scalable deployment in practical surveillance and organizational emotion analytics scenarios. The abstract clearly states that the study introduces a hybrid EPSO-RNN model, offering new insight into combining feature selection and temporal learning for improved group emotion recognition in real-world conditions