FaceFusion: Exploiting Full Spectrum of Multiple Datasets
Chiyoung Song Dongjae Lee
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.