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Recent works have shown excellent performance in overall recognition accuracy, but its accuracy significantly decreases when recognizing similar expressions. This is due to interclass homogeneity and intraclass heterogeneity. To address these issues, we propose a novel dual\u2010stage network called DUAL, inspired by contrastive learning. First, we increase the distance between negative samples while reducing the distance between positive ones. This is achieved by dynamically updating pairs of comparison samples. Second, we introduce a two\u2010stage network architecture. The first stage uses two branches to extract image features and facial keypoint features. These branches interact to learn coarse\u2010grained features through mutual guidance. The second stage focuses on fine\u2010grained features using scale\u2010specific residual blocks. This allows the model to identify facial regions that are critical for recognizing expressions. 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