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Detecting discomfort in infants is an important topic for their well-being and development. In this paper, we present an automatic and continuous video-based system for monitoring and detecting discomfort in infants. The proposed system employs a novel and efficient 3D convolutional neural network (CNN), which achieves an end-to-end solution without the conventional face detection and tracking steps. In the scheme of this study, we thoroughly investigate the video characteristics (e.g.,doi:10.21037/qims-20-1302 pmid:34249635 pmcid:PMC8250023 fatcat:pm2srgp24bgidg5i46rjpnqhty