Browse State-of-the-Art › Lightweight Face Recognition
Lightweight Face Recognition
12 papers with code · 7 benchmarks · 6 datasets archive 2025-07-28
Lightweight Face Recognition models are a group of face recognition models with lightweight backbones, which can be used for mobile or edge device applications.
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
7 leaderboard tables shown for this task, 7 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
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| LFW (6 rows) | EdgeFace - S (g=0.5) | EdgeFace: Efficient Face Recognition Model for Edge Devices | code | Syntology ran 2 of 2 samples · 0 unverified | Compare |
| AgeDB-30 (5 rows) | EdgeFace - S (g=0.5) | EdgeFace: Efficient Face Recognition Model for Edge Devices | code | Syntology ran 2 of 2 samples · 0 unverified | Compare |
| CFP-FP (4 rows) | EdgeFace - S (g=0.5) | EdgeFace: Efficient Face Recognition Model for Edge Devices | code | Syntology ran 2 of 2 samples · 0 unverified | Compare |
| CALFW (3 rows) | EdgeFace - S (g=0.5) | EdgeFace: Efficient Face Recognition Model for Edge Devices | code | Syntology ran 2 of 2 samples · 0 unverified | Compare |
| CPLFW (3 rows) | EdgeFace - S (g=0.5) | EdgeFace: Efficient Face Recognition Model for Edge Devices | code | Syntology ran 2 of 2 samples · 0 unverified | Compare |
| IJB-B (3 rows) | EdgeFace - S (g=0.5) | EdgeFace: Efficient Face Recognition Model for Edge Devices | code | Syntology ran 2 of 2 samples · 0 unverified | Compare |
| IJB-C (3 rows) | EdgeFace - S (g=0.5) | EdgeFace: Efficient Face Recognition Model for Edge Devices | code | Syntology ran 2 of 2 samples · 0 unverified | Compare |
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
6 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
12 shown of 12 papers with code (14 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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20 Apr 2018 17 repositories listed Syntology ran 14 of 40 samples · 26 unverified · 9 pointer-only (licence)Face Analysis Project on MXNet
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10 Apr 2023 7 repositories listedThe 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.
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24 Aug 2019 6 repositories listed Syntology ran 7 of 17 samples · 10 unverified · 5 pointer-only (licence)Therefore, designing lightweight networks with low memory requirement and computational cost is one of the most practical solutions for face verification on mobile platform.
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4 Jul 2023 3 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)In this paper, we present EdgeFace, a lightweight and efficient face recognition network inspired by the hybrid architecture of EdgeNeXt.
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11 Oct 2019 3 repositories listedTo improve the discriminative and generalization ability of lightweight network for face recognition, we propose an efficient variable group convolutional network called VarGFaceNet.
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28 Aug 2023 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)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.
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25 May 2019 2 repositories listedIn 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.
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8 Aug 2023 1 repository listedTo drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the…
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21 Jun 2022 1 repository listedDeep learning-based face recognition models follow the common trend in deep neural networks by utilizing full-precision floating-point networks with high computational costs.
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24 Aug 2021 1 repository listedHowever, this limits the deployment of such models that contain an extremely large number of parameters to embedded and low-end devices.
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27 Jul 2021 1 repository listedIn 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.
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2 Sep 2019 1 repository listedLarge scale face recognition is challenging especially when the computational budget is limited.
Syntology lines on 4 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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