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MaskOut: A Data Augmentation Method for Masked Face Recognition
IEEE/CVF International Conference on Computer Vision (ICCVW) ; : 1450-1455, 2021.
Article in English | Web of Science | ID: covidwho-1699840
ABSTRACT
Deep learning methods have achieved great performances in face recognition. However, the performances of deep learning methods deteriorate in case of wearing a mask. Recently, due to the world-wide COVID-19 pandemic, masked face recognition attracts more attention. It is non-trivial and urgent to improve the performances in masked face recognition. In this work, a simple and effective data augmentation method, named MaskOut, is proposed. MaskOut replaces a random region below the nose of a face with a random mask template to mask out original face features. Our method is computing and memory efficient and convenient to combine with other methods. The experimental results show that the performances in masked face recognition are improved by a large margin with MaskOut. Besides, we construct a real-life masked face dataset, named MCPRL-Mask, to evaluate the performance of masked face recognition models.

Full text: Available Collection: Databases of international organizations Database: Web of Science Language: English Journal: CVF International Conference on Computer Vision (ICCVW) Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Web of Science Language: English Journal: CVF International Conference on Computer Vision (ICCVW) Year: 2021 Document Type: Article