Comparativa entre ResNet-50, VGG-16, vision transformer y swin transformer para el reconocimiento facial con oclusión de una mascarilla - Tafur Acenjo, Brenda Xiomara (2023)
In the context of the pandemic, face recognition became important as a non-physical contact verification method. Thus, this research evaluated the accuracy of pre-trained models (VGG-16, RESNET-50, Vision Transformer, Swin Transformer) for identity verification, facing the challenge of mask occlusion. The results revealed that the transformer models outperformed the CNNs in accuracy. This work contributes significantly by exploring two types of architectures and creating a public dataset, enriching computer vision research for face recognition with mask occlusion.