A novel hybrid generation technique of facial expressions using fine-tuning and auxiliary condition generative adversarial network

The facial expression generation continues to be interesting for researchers and scientists worldwide. Facial expressions are an excellent way of transmitting one's emotions or intentions to others, and they have been extensively studied in areas such as driver safety, human-computer interaction (HCI), deception detection, health care, and monitoring. The facial expression generation starts with a single neutral image and generates sequences of facial expression images, which are combined to create a video. Previous methods generated facial expression images of the same person. However, they still suffer from low accuracy and image quality. This article overcomes this problem using a novel hybrid model for facial expression video generation using fine-tuning and condition-generative model architectures to optimize the model's parameters. Results indicate that the proposed novel approach significantly improves the expression generation of the same person. The proposed method can reliably and accurately generate facial expressions, with a testing accuracy of 98.7% and a training accuracy of 99.9%.