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Boosting Fairness for Masked Face Recognition
IEEE/CVF International Conference on Computer Vision (ICCVW) ; : 1531-1540, 2021.
Article in English | Web of Science | ID: covidwho-1699347
ABSTRACT
Face recognition achieved excellent performance in recent years. However, its potential for unfairness is raising alarm. For example, the recognition rate for the special group of East Asian is quite low. Many efforts have spent to improve the fairness of face recognition. During the COVID-19 pandemic, masked face recognition is becoming a hot topic but brings new challenging for fair face recognition. For example, the mouth and nose are important to recognizing faces of Asian groups. Masks would further reduce the recognition rate of Asian faces. To this end, this paper proposes a fair masked face recognition system. First, an appropriate masking method is used to generate masked faces. Men, a data re-sampling approach is employed to balance the data distribution and reduce the bias based on the analysis of training data. Moreover, we propose an asymmetric-arc-loss which is a combination of arc-face loss and circle-loss, it is useful for increasing recognition rate and reducing bias. Integrating these techniques, this paper obtained fairer and better face recognition results on masked faces.

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