A Survey on Automatic Face Recognition Using Side-View Face Images

Abstract

Face recognition from side-view positions poses a considerable challenge in automatic face recognition tasks. Pose variation up to the side-view is an issue of difference in appearance and visibility since only one eye is visible at the side-view poses. Traditionally overlooked, recent advancements in deep learning have brought side-view poses to the forefront of research attention. This survey comprehensively investigates methods addressing pose variations up to side-view and categorizes research efforts into feature-based, image-based, and set-based pose handling. Unlike existing surveys addressing pose variations, our emphasis is specifically on extreme poses. We report numerous promising innovations in each category and contemplate the utilization and challenges associated with side-view. Furthermore, we introduce current datasets and benchmarks, conduct performance evaluations across diverse methods, and explore their unique constraints. Notably, while feature-based methods currently stand as the state-of-the-art, our observations suggest that cross-dataset evaluations, attempted by only a few researchers, produce worse results. Consequently, the challenge of matching arbitrary poses in uncontrolled settings persists.