Advancements in Facial Expression Recognition: State-of-the-Art Techniques and Innovations - Babisha, A.; Swaminathan, A.; Anuradha, D.; Gnanaprakasam, C.; Kalaichelvi, T. (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Utilizing Machine Learning and Convolutional Neural Networks, researchers have unlocked the capacity to excel in identifying emotions by leveraging the potent and instinctive nature of facial expressions, a robust medium for individuals to communicate their feelings and aims. The development of efficient recognition systems is essential for enhancing human-computer interaction. However, the field of facial expression recognition encompasses various methodologies that can significantly impact the performance of facial recognition systems. In this study, we present a cutting-edge achievement of 95% accuracy on the FER2013 dataset, achieved by implementing a combination of innovative techniques inspired by recent research. In addition, we present innovative techniques aimed at boosting precision by amalgamating established CNN structures like VGG-16 and ResNet-50 with supplementary datasets like JAFFE and CK. In our endeavor to forecast emotions, we adopt an alternative method leveraging geometric attributes and facial landmarks to calculate and relay a feature set to an SVM model. Our findings unequivocally demonstrate the superiority of the ResNet50 model over others in real-time emotion prediction, significantly enhancing the system's accuracy.