Image Similarity between Masked and Unmasked Face for Consumer Electronics Applications
Abstract: Face recognition has become essential as a convenient biometric-based solution for a plethora of different consumer electronics applications, including access control systems, intelligent environments, smartphone authentication systems and so on. Early in 2020, the COVID-19 pandemic caused the widespread use of face masks, which become essential for containing the outbreak. The masks cause a visible alteration in facial appearance, covering almost the 50% of the human face. In this work, an image similarity technique is applied to assess the difference between two images of the same face wearing or not wearing a face mask. Cosine Similarity measure-based Algorithm (CSA) was used to objectively infer the difficulties that modern facial recognition algorithms, based on deep learning techniques, encounter when dealing with a masked face.
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