ULN: An efficient face recognition method for person wearing a mask.
Multimed Tools Appl
; 81(29): 42393-42411, 2022.
Article
in English
| MEDLINE | ID: covidwho-2128967
ABSTRACT
Although the face recognition has advanced by leaps and bounds in recent years, recognizing faces with large occlusion, e.g., masks, is still a challenging problem. In the context of the COVID-19 outbreak, wearing masks becomes mandatory, which fails numerous face attendance and surveillance systems. Therefore, a robust face recognition algorithm that can deal with facial masks is urgently needed. To build a mask-robust face recognition algorithm, we first generate numerous facial images with masks based on public face datasets, which obviously alleviates the problem of the training data shortage. Second, we propose a novel network architecture called Upper-Lower Network (ULN) to recognize the faces with masks efficiently. The upper branch of ULN with the mask-free images as input is pretrained that provides supervisory information for the training of the lower branch. Considering that the occlusion areas of masks usually appear in the lower parts of faces, we further divide the high-order semantic features into upper and lower parts. The designed loss function force the learned features of the lower branch similar to those of the upper branch with the same mask-free image inputs, but only the upper part of features similar to the mask counterparts. Extensive experiments demonstrate that the proposed method is effective for recognizing persons with masks and outperforms other state-of-the-art face recognition methods.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Language:
English
Journal:
Multimed Tools Appl
Year:
2022
Document Type:
Article
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