Dynamic deformable transformer for end‐to‐end face alignment

Abstract

Heatmap‐based regression (HBR) methods have dominated for a long time in the face alignment field while these methods need complex design and post‐processing. In this study, the authors propose an end‐to‐end and simple enough coordinate‐based regression (CBR) method called Dynamic Deformable Transformer (DDT) for face alignment. Unlike general pre‐defined landmark queries, DDT uses Dynamic Landmark Queries (DLQs) to query landmarks' classes and coordinates together. Besides, DDT adopts a deformable attention mechanism rather than a regular attention mechanism which has a faster convergence speed and lower computational complexity. Experiment results on three mainstream datasets 300W, WFLW, and COFW demonstrate DDT exceeds the state‐of‐the‐art CBR methods by a large margin and is comparable to the current state‐of‐the‐art HBR methods with much less computational complexity.