Micro-expressions (MEs) are important clues for reflecting the real feelings of humans, and micro-expression recognition (MER) can thus be applied in various real-world applications. However, it is difficult to perceive and interpret MEs correctly. With the advance of deep learning technologies, the accuracy of micro-expression recognition is improved but still limited by the lack of large-scale datasets. In this paper, we propose a novel micro-expression recognition approach by combining Action Units (AUs) and emotion category labels. Specifically, based on facial muscle movements, we model different AUs based on relational information and integrate the AUs recognition task with MER. Besides, to overcome the shortcomings of limited and imbalanced training samples, we propose a data augmentation method that can generate nearly indistinguishable image sequences with AU intensity of real-world micro-expression images, which effectively improve the performance and are compatible with other micro-expression recognition methods. Experimental results on three mainstream micro-expression datasets, i.e., CASME II, SAMM, and SMIC, manifest that our approach outperforms other state-of-the-art methods on both single database and cross-database micro-expression recognition. CCS CONCEPTS • Computing methodologies → Neural networks; Computer vision; Image representations.

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Pedestrian detection when occlusions exist represents a great challenge in real-world applications, including urban autonomous driving and surveillance systems. However, the head-shoulder feature of pedestrians, which is more stable and less likely to be occluded than other areas of the body, can be used as a complement to full body prediction to boost pedestrian detection accuracy. In this paper, we investigate the unique features of the head-shoulder and full body features belonging to pedestrians. Then, instead of using a popular general object detection framework like R-CNN series, SSD, or YOLO, we propose a novel pedestrian detection network, called PedJointNet, that simultaneously regresses two bounding boxes to localize the head-shoulder and full body regions based on a feasible object detection backbone. Moreover, unlike the traditional strategy of keeping the weights fixed for each attribute, we design an inbuilt mechanism to dynamically and adaptively adjust the relationships of the head-shoulder and full body predictions for more accurate pedestrian localization. We validate the effectiveness of the proposed method using the CUHK-SYSU, TownCentre, and CityPersons datasets. Overall, our two-pronged prediction approach achieves excellent performance in detecting both non-occluded and occluded pedestrians, especially under circumstances involving occlusion, as compared to other state-of-the-art methods.INDEX TERMS Pedestrian detection, head-shoulder detection, adaptively adjusted weights.

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