Facial Expression and Key Landmarks Detection using Deep Learning Techniques

The recognition of facial expressions is a crucial topic in the field of computer vision and artificial intelligence due to its significant contribution in providing an essential understanding of human emotions and motivations. However, accurately detecting human emotions through algorithms poses a significant challenge because facial expressions are interpreted as mathematical patterns. The training capabilities of conventional machine learning techniques are limited, leading to reduced rates of recognition. The present study focuses on utilizing transfer learning methodologies to enhance emotion identification. Notably, the authors achieved significant accuracy rates by utilizing pre-trained ResNet-50, VGG-19, and Inception V3 networks. The ResNet-50 model achieved a 98% accuracy rate, while the VGG-19 and Inception V3 models attained 96% and 92% accuracy rates, respectively. The use of pre-trained models with their corresponding pre-trained weights resulted in improved accuracy of facial expression recognition. Furthermore, the study results were compared to conventional machine learning methods of the past, and the proposed transfer learning approach exhibited enhanced precision in identifying facial expressions. The findings of the study suggest that the utilization of transfer learning techniques can significantly improve the accuracy of models designed for recognizing facial expressions. Facial expression recognition holds significant promise in a variety of domains, such as emotion analysis, human-computer interaction, and security systems. This investigation highlights the potential of transfer learning methodologies in advancing the field of facial expression recognition, thereby facilitating a better comprehension of human emotion and motivation.