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.
@cite_21: In this paper we make the first effort, to the best of our knowledge, to combine multiple face landmark datasets with different landmark definitions into a super dataset, with a union of all landmark types computed in each image as output. Our approach is flexible, and our system can optionally use known landmarks in the target dataset to constrain the localization. Our novel pipeline is built upon variants of state-of-the-art facial landmark localization methods. Specifically, we propose to label images in the target dataset jointly rather than independently and exploit exemplars from both the source datasets and the target dataset. This approach integrates nonparametric appearance and shape modeling and graph matching together to achieve our goal.
It is worth noting that in the recent work by Smith and Zhang @cite_21 , they have independently presented an alternative way to combine multiple face landmark datasets with different landmark definitions into a super dataset.