Learning 3D Face Representation with Vision Transformer for Masked Face Recognition

Masked face recognition, a non-contact biometric technology, has attracted much attention and developed rapidly during the coronavirus disease 2019 (COVID-19) outbreak. The existing work trains the masked face recognition model based on a large number of 2D masked face images. However, in practical application scenarios, it is difficult to obtain a large number of masked face images in a short period of time. Therefore, combined with 3D face recognition technology, this paper proposes a masked face recognition model trained with non-masked face images. In this paper, we locate and segment the complete face region and the face region not occluded by masks from the face point clouds. The geometric features of the 3D face surface, namely depth, azimuth, and elevation, are extracted from the above two regions to generate training data. The proposed masked face recognition model based on vision Transformer divides the complete faces and part of the faces into sequence images, and then captures the relationship between the image slices to compensate for the impact caused by the lack of face information, thereby improving the recognition performance. Comparative experiments with the state-of-the-art masked face recognition work are carried out on four databases. The experimental results show that the recognition accuracy of the proposed model is improved by 9.86% on Bosphorus database, 16.77% on CASIA-3D FaceV1 database, 2.32% on StirlingESRC database, and 34.81% on Ajmal main database, respectively, which verifies the effectiveness and stability of the proposed model. © 2022 IEEE.

Face Recognition Using Fisherface and Support Vector Machine Method

COVID-19 is declared as a pandemic by WHO and until now COVID-19 pandemic remains a problem in 2021. Many efforts have been made to reduce the spreading virus, one way to reduce its spread is by wearing a mask but most people often ignore it. Monitoring large groups of people becomes difficult by the government or the authorities. Face recognition, a biometric technology, is based on the identification of a face features of a person. This paper describes a face recognition using Fisherface and Support Vector Machine method to classify face mask dataset. Face recognition using Fisherface method is based on Principal Component Analysis (PCA) and Fisher's Linear Discriminant (FLD) method or also known as Linear Discriminant Analysis (LDA). The algorithm used in the process for feature extraction is Fisherface algorithm while classification using Support Vector Machine method. The results show that for face recognition on face mask dataset using cross validation with 10 fold, the average percentage accuracy is 99.76%. © 2021 IEEE.