FaceFusion: Exploiting Full Spectrum of Multiple Datasets
This addresses the challenge of efficiently building large, clean datasets for face recognition, though it appears incremental as it improves on existing multi-dataset training approaches.
The paper tackles the problem of label noise when combining multiple face recognition datasets by introducing FaceFusion, a method that creates a conflict-free fused view for training, resulting in a noticeable performance boost that surpasses single-dataset and previous methods in public evaluations.
The size of training dataset is known to be among the most dominating aspects of training high-performance face recognition embedding model. Building a large dataset from scratch could be cumbersome and time-intensive, while combining multiple already-built datasets poses the risk of introducing large amount of label noise. We present a novel training method, named FaceFusion. It creates a fused view of different datasets that is untainted by identity conflicts, while concurrently training an embedding network using the view in an end-to-end fashion. Using the unified view of combined datasets enables the embedding network to be trained against the entire spectrum of the datasets, leading to a noticeable performance boost. Extensive experiments confirm superiority of our method, whose performance in public evaluation datasets surpasses not only that of using a single training dataset, but also that of previously known methods under various training circumstances.