  Face recognition of realistic visual images has been well studied and made a
significant progress in the recent decade. Unlike the realistic visual images,
the face recognition of the caricatures is far from the performance of the
visual images. This is largely due to the extreme non-rigid distortions of the
caricatures introduced by exaggerating the facial features to strengthen the
characters. The heterogeneous modalities of the caricatures and the visual
images result the caricature-visual face recognition is a cross-modal problem.
In this paper, we propose a method to conduct caricature-visual face
recognition via multi-task learning. Rather than the conventional multi-task
learning with fixed weights of tasks, this work proposes an approach to learn
the weights of tasks according to the importance of tasks. The proposed
multi-task learning with dynamic tasks weights enables to appropriately train
the hard task and easy task instead of being stuck in the over-training easy
task as conventional methods. The experimental results demonstrate the
effectiveness of the proposed dynamic multi-task learning for cross-modal
caricature-visual face recognition. The performances on the datasets CaVI and
WebCaricature show the superiority over the state-of-art methods.
