Micro-Expressions (MEs) are involuntary and im-perceptible facial movements that reflect the underlying emotions and inner activities. Recently, ME recognition technology has been widely used in several fields such as medical treatment. Due to the subtle variations among the video sequence and the limited training data, the ME recognition task still remains a challenging problem. Existing methods tend to address the ME recognition problem from two aspects: (1) Data augmentation and (2) Expression signal amplification. Few works realize the importance of temporal variation hidden in the ME sequence. Based on the above observation, we propose a Graph Contrastive Learning (GCL) framework to effectively perceive subtle temporal variation for robust ME recognition. Specifically, the strong spatial feature representation is captured through the transformer-based ME feature encoder. Then, the proposed GCL builds the graph structure for the ME sequence and introduces the graph convolution to model the temporal relationship. To capture and highlight the temporal variation hidden in the ME sequence, a contrastive learning framework is designed to discriminately learn the differences between the normal and the abnormal ME samples. Both quantitative and qualitative experimental results show the effectiveness and superiority of our method compared with the prior state-of-the-arts.

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. 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.