Enhancing the Performance of Facial Recognition Technology

Facial Expression Recognition became popular both in research and in industry. We’ll discuss the fascinating use of computer vision in this project. In the context of smart interaction between people and computers, the ability to recognize human emotion from facial expressions is extremely important. However, several characteristics, including inadequate lighting, quick actions, stances and perspectives, and occlusion influence the precision of facial emotion identification. Convolutional neural network and deep neural network models can be improved because they are continuously trained on fresh data sets, and are frequently more extensive and of more excellent quality. In this article, we offer a technique for feature extraction using the deep residual network ResNet-50, which integrates convolutional neural networks for the identification of facial emotions. Accuracy, precision, recall, and F1-score are just a few of the performance indicators that this micro document delivers a thorough examination of for every model. It accurately recognizes emotions and can help facial expression recognition algorithms become more accurate.