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

Cross-modal Multi-task Learning for Graphic Recognition of Caricature Face