Open-Set Profile-to-Frontal Face Recognition on a Very Limited Dataset

Open-set face recognition on a small dataset with limited image samples per individual poses a significant challenge and is a topic of active research. Therefore, this study investigated the problems of open-set face verification and face identification on a dataset known as the ITB Frontal Profile Limited Dataset (IFPLD), which included only one frontal and one profile image per individual. Various training procedures were used to obtain a more appropriate network embedding for feature representation on the dataset. Transfer learning was employed to improve the performance of the models by fine-tuning the networks using a dataset with properties similar to those of the IFPLD. The results showed that the SimCLR method generated the optimal network embedding for face verification on the Siamese network. The prototypical network with an N-way-k-shot learning scenario where k-1 came from data augmentation outperformed the Siamese network for face identification by a maximum 17.0% accuracy improvement. The transformation from 1-shot learning to k-shot learning is critical for achieving high performance.