Graph Jigsaw Learning for Cartoon Face Recognition, TIP 2022

Cartoon face recognition is challenging as they typically have smooth color regions and emphasized edges, the key to recognizing cartoon faces is to precisely perceive their sparse and critical shape patterns. we propose the GraphJigsaw that constructs jigsaw puzzles at various stages in the classification network and solves the puzzles with the graph convolutional network (GCN) progressively. GraphJigsaw significantly enhance the Cartoon face recognition accuracy with no extra manual annotation during training and no extra computation burden during inference. We hope GraphJigsaw will shed light on understanding and improving the performance of cartoon face recognition.

Main idea of GraphJigsaw.

GraphJigsaw takes the input/output of the 1st/2nd/3rd/4th stage in the backbone network (e.g., ResNet-50 or DenseNet-169) as the input/target. GraphJigsaw takes $\mathbf{X}^3_{\text{in}}$ as input and constructs a shuffled graph, then solves the jigsaw puzzle in the face graph encoding and decoding process. GraphJigsaw can be incorporated in each stage in the backbone network in a top-down manner, thus the valuable shape patterns of the input cartoon faces can be reserved in the early stages and strengthened in the later stages gradually.

Prerequisites

• Python 2.x
• Pytorch 0.4.x or above

If you use this code in your paper, please cite the following:

@ARTICLE{9786555,
  author={Li, Yong and Lao, Lingjie and Cui, Zhen and Shan, Shiguang and Yang, Jian},
  journal={IEEE Transactions on Image Processing}, 
  title={Graph Jigsaw Learning for Cartoon Face Recognition}, 
  year={2022},
  volume={31},
  number={},
  pages={3961-3972},
  doi={10.1109/TIP.2022.3177952}
  }