MFCosface: A Masked-Face Recognition Algorithm Based on Large Margin Cosine Loss

Abstract

The world today is being hit by COVID-19. As opposed to fingerprints and ID cards, facial recognition technology can effectively prevent the spread of viruses in public places because it does not require contact with specific sensors. However, people also need to wear masks when entering public places, and masks will greatly affect the accuracy of facial recognition. Accurately performing facial recognition while people wear masks is a great challenge. In order to solve the problem of low facial recognition accuracy with mask wearers during the COVID-19 epidemic, we propose a masked-face recognition algorithm based on large margin cosine loss (MFCosface). Due to insufficient masked-face data for training, we designed a masked-face image generation algorithm based on the detection of the detection of key facial features. The face is detected and aligned through a multi-task cascaded convolutional network; and then we detect the key features of the face and select the mask template for coverage according to the positional information of the key features. Finally, we generate the corresponding masked-face image. Through analysis of the masked-face images, we found that triplet loss is not applicable to our datasets, because the results of online triplet selection contain fewer mask changes, making it difficult for the model to learn the relationship between mask occlusion and feature mapping. We use a large margin cosine loss as the loss function for training, which can map all the feature samples in a feature space with a smaller intra-class distance and a larger inter-class distance. In order to make the model pay more attention to the area that is not covered by the mask, we designed an Att-inception module that combines the Inception-Resnet module and the convolutional block attention module, which increases the weight of any unoccluded area in the feature map, thereby enlarging the unoccluded area’s contribution to the identification process. Experiments on several masked-face datasets have proved that our algorithm greatly improves the accuracy of masked-face recognition, and can accurately perform facial recognition with masked subjects.

Masked Face Recognition Using Deep Learning: A Review

“…Deng et al [85] have also proposed MFCosface as a MFR algorithm on the basis of the large margin cosine loss. It efficiently overcomes the problem of low recognition rates with mask occlusions by detecting the key facial features of masked faces.…”

“…VGG-Face2_m [85] is a new version of the VGG-Face dataset. It contains over 3.3 million images of 9131 identities.…”

“…It contains over 3.3 million images of 9131 identities. CASIA-FaceV5_m [85] is a refined version of CASIA-FaceV5, which contains 2500 images of 500 Asian people, with five images for each person.…”

“…They used ResNet to extract features, which were evaluated on RMFRD and SMFRD datasets. Deng et al [85] proposed the MFCosface MFR algorithm, based on large margin cosine loss, to detect the key facial features optimized by adding an attention-aware mechanism in the model. Li et al [177] also proposed an attentionbased algorithm and a cropping-based algorithm for MFR.…”

“…MaskTheFace [31] MaskedFace-Net [32], DCNN [33], CYCLE-GAN [34], IAMGAN [35], starGAN [36], segments [39][40][41], regularization [42], sparse rep. [43] Domain-specific models FaceNet [83], SphereFace [8], MFCosface [85], VGGFace [48], DeepID [86], LSTM-autoencoders [70], DC-SSDA [71], de-corrupt autoencoders [72], 3D autoencoder [73], pose invariant FR [77], makeup-invariant [78], DBNs [79,80], attention-aware [82], margin-aware [15] Feature extraction LBPs [44], SIFT [45], HOG [89], codebooks [90], multi-stage mask learning strategy [92], attention-aware and context-aware [93][94][95], GCN [96][97][98] Mask detection R-CNN [101], Fast R-CNN [102], Faster R-CNN [103], context-attention R-CNN [104], FCN [105], U-Net [106], FAN [109], LLE-CNNs [110], ...…”