Revisit of Region-Feature Combinations in Facial Analysis

Nie, Zhongliang; Mattey, Akhil; Huang, Zhe; Nguyên, Tam

doi:10.1109/smc.2018.00403

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2021

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“…Many prior works have focused on facial recognition in cases of occlusion [ 41 , 42 ]. The work has been conducted in a number of ways, including identification of the face in the wild, twin recognition [ 43 ], occluded face detection [ 41 , 42 ], detecting the face between the mask and the actual face, and the use of Generative Adversarial Networks (GANs) for face modulation and detection. There are also a few studies of detecting faces with masks.…”

Section: Related Workmentioning

confidence: 99%

Masked Face Analysis via Multi-Task Deep Learning

Patel

Nie

et al. 2021

J. Imaging

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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 learning model to tackle the problem. In particular, the multi-task deep learning model takes the data as inputs and shares their weight to yield predictions of age, expression, and gender for the masked face. Through extensive experiments, the proposed framework has been found to provide a better performance than other existing methods.

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