  Although face analysis has achieved remarkable improvements in the past few
years, designing a multi-task face analysis model is still challenging. Most
face analysis tasks are studied as separate problems and do not benefit from
the synergy among related tasks. In this work, we propose a novel task-adaptive
multi-task face analysis method named as Q-Face, which simultaneously performs
multiple face analysis tasks with a unified model. We fuse the features from
multiple layers of a large-scale pre-trained model so that the whole model can
use both local and global facial information to support multiple tasks.
Furthermore, we design a task-adaptive module that performs cross-attention
between a set of query vectors and the fused multi-stage features and finally
adaptively extracts desired features for each face analysis task. Extensive
experiments show that our method can perform multiple tasks simultaneously and
achieves state-of-the-art performance on face expression recognition, action
unit detection, face attribute analysis, age estimation, and face pose
estimation. Compared to conventional methods, our method opens up new
possibilities for multi-task face analysis and shows the potential for both
accuracy and efficiency.
