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Masked Face Analysis via Multi-Task Deep Learning
2021
Journal of Imaging
Face recognition with wearable items has been a challenging task in computer vision and involves the problem of identifying humans wearing a face mask. Masked face analysis via multi-task learning could effectively improve performance in many fields of face analysis. In this paper, we propose a unified framework for predicting the age, gender, and emotions of people wearing face masks. We first construct FGNET-MASK, a masked face dataset for the problem. Then, we propose a multi-task deep
doi:10.3390/jimaging7100204
pmid:34677290
pmcid:PMC8539947
fatcat:qd663hk45zdxnowvbe7unedj6m