Face identification for Masked and Unmasked Faces using an automated masked training set

IPEM Journal Computer Applications > Face identification for Masked and Unmasked Faces using an automated masked training set

Face identification for Masked and Unmasked Faces using an automated masked training set

Rahul Dhandhukia, Monika Shah

Vol 7 , Issue 1 , December 2022 | Pages: 34-43

Published Online: December, 2022

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Author Details

1. Rahul Dhandhukia, Institute of Technology, Nirma University, Gujarat, India
2. Monika Shah, Institute of Technology, Nirma University, Gujarat, India

Facial recognition is a popular biometric authentication technique that is adopted everywhere. For the sustainability of the societal community, the COVID-19 pandemic and following viral attacks have mandated the wearing of masks at all public events and organizations. It set new standards for how existing facial recognition-based biometric identification systems must function in order to identify faces hidden behind masks. To meet this need, the researchers have attempted fixing the current algorithms and constructing new ones. Each has one or more drawbacks, such as the difficulty of identifying faces in masked images of various orientations, the expense of building a masked training set, the need to train systems using various mask colors and shapes, etc. Most tedious part here is to re-generate a dataset with different masks. This work uses machine learning and deep learning algorithms to automate the development of masked training sets and propose a model to recognize a person regardless of whether they are wearing a mask or not. With the aid of this function, industries may easily transition to new face recognition effectively

Keywords

Masked Face Recognition, Computer vision, Mask augmentation, Face recognition, Face detection, Biometric, Occlusion
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