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http://dx.doi.org/10.6109/jkiice.2022.26.8.1136

Generation of Masked Face Image Using Deep Convolutional Autoencoder  

Lee, Seung Ho (Department of Future Technology, Korea University of Technology and Education)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.8, 2022 , pp. 1136-1141 More about this Journal
Abstract
Researches of face recognition on masked faces have been increasingly important due to the COVID-19 pandemic. To realize a stable and practical recognition performance, large amount of facial image data should be acquired for the purpose of training. However, it is difficult for the researchers to obtain masked face images for each human subject. This paper proposes a novel method to synthesize a face image and a virtual mask pattern. In this method, a pair of masked face image and unmasked face image, that are from a single human subject, is fed into a convolutional autoencoder as training data. This allows learning the geometric relationship between face and mask. In the inference step, for a unseen face image, the learned convolutional autoencoder generates a synthetic face image with a mask pattern. The proposed method is able to rapidly generate realistic masked face images. Also, it could be practical when compared to methods which rely on facial feature point detection.
Keywords
Facial Mask Synthesis; Facial Mask Generation; Convolutional Autoencoder; Face Recognition;
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