  Cartoon face recognition is challenging as they typically have smooth color
regions and emphasized edges, the key to recognize cartoon faces is to
precisely perceive their sparse and critical shape patterns. However, it is
quite difficult to learn a shape-oriented representation for cartoon face
recognition with convolutional neural networks (CNNs). To mitigate this issue,
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) in a progressive manner. Solving the puzzles requires the model
to spot the shape patterns of the cartoon faces as the texture information is
quite limited. The key idea of GraphJigsaw is constructing a jigsaw puzzle by
randomly shuffling the intermediate convolutional feature maps in the spatial
dimension and exploiting the GCN to reason and recover the correct layout of
the jigsaw fragments in a self-supervised manner. The proposed GraphJigsaw
avoids training the classification model with the deconstructed images that
would introduce noisy patterns and are harmful for the final classification.
Specially, GraphJigsaw can be incorporated at various stages in a top-down
manner within the classification model, which facilitates propagating the
learned shape patterns gradually. GraphJigsaw does not rely on any extra manual
annotation during the training process and incorporates no extra computation
burden at inference time. Both quantitative and qualitative experimental
results have verified the feasibility of our proposed GraphJigsaw, which
consistently outperforms other face recognition or jigsaw-based methods on two
popular cartoon face datasets with considerable improvements.
