MixFaceNets: Extremely Efficient Face Recognition Networks
TLDR
MixFaceNets as discussed by the authors is a set of extremely efficient and high throughput models for accurate face verification, which are inspired by Mixed Depthwise Convolutional Kernels (MDCK).Abstract:
In this paper, we present a set of extremely efficient and high throughput models for accurate face verification, Mix-FaceNets which are inspired by Mixed Depthwise Convolutional Kernels. Extensive experiment evaluations on Label Face in the Wild (LFW), Age-DB, MegaFace, and IARPA Janus Benchmarks IJB-B and IJB-C datasets have shown the effectiveness of our MixFaceNets for applications requiring extremely low computational complexity. Under the same level of computation complexity (≤ 500M FLOPs), our MixFaceNets outperform MobileFaceNets on all the evaluated datasets, achieving 99.60% accuracy on LFW, 97.05% accuracy on AgeDB-30, 93.60 TAR (at FAR1e-6) on MegaFace, 90.94 TAR (at FAR1e-4) on IJB-B and 93.08 TAR (at FAR1e-4) on IJB-C. With computational complexity between 500M and 1G FLOPs, our MixFaceNets achieved results comparable to the top-ranked models, while using significantly fewer FLOPs and less computation over-head, which proves the practical value of our proposed Mix-FaceNets. All training codes, pre-trained models, and training logs have been made available https://github.com/fdbtrs/mixfacenets.read more
Citations
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Proceedings ArticleDOI
MFR 2021: Masked Face Recognition Competition
Fadi Boutros,Naser Damer,Jan Niklas Kolf,Kiran B. Raja,Florian Kirchbuchner,Raghavendra Ramachandra,Arjan Kuijper,Pengcheng Fang,Chao Zhang,Fei Wang,David Montero,Naiara Aginako,Basilio Sierra,Marcos Nieto,Mustafa Ekrem Erakin,Ugur Demir,Hazim Kemal Ekenel,Asaki Kataoka,Kohei Ichikawa,Shizuma Kubo,Jie Zhang,Mingjie He,Dan Han,Shiguang Shan,Klemen Grm,Vitomir Struc,Sachith Seneviratne,Nuran Kasthuriarachchi,Sanka Rasnayaka,Pedro C. Neto,Ana F. Sequeira,Joao Ribeiro Pinto,Mohsen Saffari,Jaime S. Cardoso +33 more
TL;DR: The Masked Face Recognition Competition (MFR) as discussed by the authors was held within the 2021 International Joint Conference on Biometrics (IJCB 2021) and attracted a total of 10 participating teams with valid submissions.
Journal ArticleDOI
Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors
Naser Damer,C'esar Augusto Fontanillo L'opez,Meiling Fang,Noémie Spiller,Minh-Khoi Pham,Fadi Boutros +5 more
TL;DR: This work introduces the first synthetic-based MAD development dataset, namely the Synthetic Morphing Attack Detection Development dataset (SMDD), which is utilized successfully to train three MAD backbones where it proved to lead to high MAD performance, even on completely unknown attack types.
Posted Content
PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation
TL;DR: In this article, a new family of face recognition models, namely PocketNet, is proposed to enhance the verification performance of the compact model by presenting a novel training paradigm based on knowledge distillation.
Journal ArticleDOI
PocketNet: Extreme Lightweight Face Recognition Network Using Neural Architecture Search and Multistep Knowledge Distillation
TL;DR: In this article , a novel training paradigm based on knowledge distillation is proposed, where the knowledge is distilled from the teacher model to the student model at different stages of the training maturity.
Posted Content
FocusFace: Multi-task Contrastive Learning for Masked Face Recognition
TL;DR: FocusFace as discussed by the authors is a multi-task architecture that uses contrastive learning to accurately perform masked face recognition, which is designed to be trained from scratch or to work on top of state-of-the-art face recognition methods without sacrificing the capabilities of existing models in conventional face recognition tasks.
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