MFR 2021: Masked Face Recognition Competition

This paper presents a summary of the Masked Face Recognition Competitions (MFR) held within the 2021 International Joint Conference on Biometrics (IJCB 2021). The competition attracted a total of 10 participating teams with valid submissions. The affiliations of these teams are diverse and associated with academia and industry in nine different countries. These teams successfully submitted 18 valid solutions. The competition is designed to motivate solutions aiming at enhancing the face recognition accuracy of masked faces. Moreover, the competition considered the deployability of the proposed solutions by taking the compactness of the face recognition models into account. A private dataset representing a collaborative, multisession, real masked, capture scenario is used to evaluate the submitted solutions. In comparison to one of the topperforming academic face recognition solutions, 10 out of the 18 submitted solutions did score higher masked face verification accuracy.

[1]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Kaiming He,et al.  Momentum Contrast for Unsupervised Visual Representation Learning , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Naser Damer,et al.  Unmasking Face Embeddings by Self-restrained Triplet Loss for Accurate Masked Face Recognition , 2021, Pattern Recognition.

[4]  Naser Damer,et al.  MixFaceNets: Extremely Efficient Face Recognition Networks , 2021, 2021 IEEE International Joint Conference on Biometrics (IJCB).

[5]  Debing Zhang,et al.  Lightweight Face Recognition Challenge , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

[6]  Arijit Raychowdhury,et al.  Masked Face Recognition for Secure Authentication , 2020, ArXiv.

[7]  Debing Zhang,et al.  Partial FC: Training 10 Million Identities on a Single Machine , 2021, 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).

[8]  Honglak Lee,et al.  Learning to Align from Scratch , 2012, NIPS.

[9]  Pengwei Wang,et al.  WearMask: Fast In-browser Face Mask Detection with Serverless Edge Computing for COVID-19 , 2021, ArXiv.

[10]  Luis Enrique Sucar,et al.  Benchmarking lightweight face architectures on specific face recognition scenarios , 2021, Artificial Intelligence Review.

[11]  Tal Hassner,et al.  Face recognition in unconstrained videos with matched background similarity , 2011, CVPR 2011.

[12]  Naser Damer,et al.  The Effect of Wearing a Mask on Face Recognition Performance: an Exploratory Study , 2020, 2020 International Conference of the Biometrics Special Interest Group (BIOSIG).

[13]  Simon Dobrisek,et al.  How to Correctly Detect Face-Masks for COVID-19 from Visual Information? , 2021, Applied Sciences.

[14]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Yang Liu,et al.  MobileFaceNets: Efficient CNNs for Accurate Real-time Face Verification on Mobile Devices , 2018, CCBR.

[16]  Ross B. Girshick,et al.  Fast and Accurate Model Scaling , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Li Liu,et al.  Cropping and attention based approach for masked face recognition , 2021, Appl. Intell..

[18]  Irene Kotsia,et al.  RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Stefanos Zafeiriou,et al.  Sub-center ArcFace: Boosting Face Recognition by Large-Scale Noisy Web Faces , 2020, ECCV.

[20]  Omkar M. Parkhi,et al.  VGGFace2: A Dataset for Recognising Faces across Pose and Age , 2017, 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018).

[21]  Samy Bengio,et al.  A Study of the Effects of Score Normalisation Prior to Fusion in Biometric Authentication Tasks , 2004 .

[22]  Xiaogang Wang,et al.  Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[23]  Naser Damer,et al.  Real Masks and Fake Faces: On the Masked Face Presentation Attack Detection , 2021, ArXiv.

[24]  Yu Qiao,et al.  Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks , 2016, IEEE Signal Processing Letters.

[25]  Naiara Aginako,et al.  Boosting Masked Face Recognition with Multi-Task ArcFace , 2021, ArXiv.

[26]  Dongxiao Li,et al.  Identifying Facemask-Wearing Condition Using Image Super-Resolution with Classification Network to Prevent COVID-19 , 2020, Sensors.

[27]  Arjan Kuijper,et al.  Extended evaluation of the effect of real and simulated masks on face recognition performance , 2021, IET Biom..

[28]  Yuxiao Hu,et al.  MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition , 2016, ECCV.

[29]  Hao Wu,et al.  Masked Face Recognition Dataset and Application , 2020, ArXiv.

[30]  Gunasekaran Manogaran,et al.  A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic , 2020, Measurement.

[31]  Stefanos Zafeiriou,et al.  ArcFace: Additive Angular Margin Loss for Deep Face Recognition , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Patrick Grother,et al.  Ongoing Face Recognition Vendor Test (FRVT) Part 6B: Face recognition accuracy with face masks using post-COVID-19 algorithms , 2020 .

[33]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[34]  Mahmoud Afifi,et al.  AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces , 2017, J. Vis. Commun. Image Represent..

[35]  Naser Damer,et al.  Biometrics in the Era of COVID-19: Challenges and Opportunities , 2021, ArXiv.