Lightweight Face Recognition
12 papers with code • 7 benchmarks • 5 datasets
Lightweight Face Recognition models are a group of face recognition models with lightweight backbones, which can be used for mobile or edge device applications.
Benchmarks
These leaderboards are used to track progress in Lightweight Face Recognition
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LFW
IJB-C
IJB-B
CALFW
Most implemented papers
MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices
Face Analysis Project on MXNet
SeesawFaceNets: sparse and robust face verification model for mobile platform
didi/AoE
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arXiv 2019
Therefore, designing lightweight networks with low memory requirement and computational cost is one of the most practical solutions for face verification on mobile platform.
GhostFaceNets: Lightweight Face Recognition Model From Cheap Operations
HamadYA/GhostFaceNets
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IEEE Access 2023
The development of deep learning-based biometric models that can be deployed on devices with constrained memory and computational resources has proven to be a significant challenge.
VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight Face Recognition
To improve the discriminative and generalization ability of lightweight network for face recognition, we propose an efficient variable group convolutional network called VarGFaceNet.
EdgeFace: Efficient Face Recognition Model for Edge Devices
otroshi/edgeface
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In this paper, we present EdgeFace, a lightweight and efficient face recognition network inspired by the hybrid architecture of EdgeNeXt.
ShrinkTeaNet: Million-scale Lightweight Face Recognition via Shrinking Teacher-Student Networks
david-svitov/margindistillation
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In addition, this work introduces a novel Angular Distillation Loss for distilling the feature direction and the sample distributions of the teacher's hypersphere to its student.
SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data
While generating synthetic datasets for training face recognition models is an alternative option, it is challenging to generate synthetic data with sufficient intra-class variations.
Towards Flops-constrained Face Recognition
liuyuisanai/trojans-face-recognizer
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Large scale face recognition is challenging especially when the computational budget is limited.
MixFaceNets: Extremely Efficient Face Recognition Networks
In this paper, we present a set of extremely efficient and high throughput models for accurate face verification, MixFaceNets which are inspired by Mixed Depthwise Convolutional Kernels.
PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation
fdbtrs/pocketnet
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However, this limits the deployment of such models that contain an extremely large number of parameters to embedded and low-end devices.
