  Dataset bias is a well known problem in object recognition domain. This
issue, nonetheless, is rarely explored in face alignment research. In this
study, we show that dataset plays an integral part of face alignment
performance. Specifically, owing to face alignment dataset bias, training on
one database and testing on another or unseen domain would lead to poor
performance. Creating an unbiased dataset through combining various existing
databases, however, is non-trivial as one has to exhaustively re-label the
landmarks for standardisation. In this work, we propose a simple and yet
effective method to bridge the disparate annotation spaces between databases,
making datasets fusion possible. We show extensive results on combining various
popular databases (LFW, AFLW, LFPW, HELEN) for improved cross-dataset and
unseen data alignment.
