# Open Access Dataset for Electromyography based Multi-code Biometric Authentication

arXiv: https://arxiv.org/abs/2201.01051 · HTML: https://mlsanity.com/paper/2201.01051/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 63 references that are in the map.
- First posted on arXiv 4 Jan 2022.

## Builds on (most cited first, 8 of 8)

- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [The MegaFace Benchmark: 1 Million Faces for Recognition at Scale](https://mlsanity.com/paper/1512.00596/) (2015)
- [Interpreting Deep Learning Features for Myoelectric Control: A Comparison with Handcrafted Features](https://mlsanity.com/paper/1912.00283/) (2019)
- [putEMG -- a surface electromyography hand gesture recognition dataset](https://mlsanity.com/paper/1907.07428/) (2019)
- [Unsupervised Domain Adversarial Self-Calibration for Electromyographic-based Gesture Recognition](https://mlsanity.com/paper/1912.11037/) (2019)
- [Expectation maximization transfer learning and its application for bionic hand prostheses](https://mlsanity.com/paper/1711.09256/) (2017)
- [Performance Optimization of Surface Electromyography (sEMG) based Biometric Sensing System for both Verification and Identification](https://mlsanity.com/paper/2103.06015/) (2021)
- [EmgAuth: Unlocking Smartphones with EMG Signals](https://mlsanity.com/paper/2103.12542/) (2021)

## Built on by (most cited first, 12 of 12)

- [Class-incremental Learning for Time Series: Benchmark and Evaluation](https://mlsanity.com/paper/2402.12035/) (2024)
- [Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in Surface Electromyographic Signal Analysis](https://mlsanity.com/paper/2404.11213/) (2024)
- [TS-ACL: Closed-Form Solution for Time Series-oriented Continual Learning](https://mlsanity.com/paper/2410.15954/) (2024)
- [Machine Learning-based sEMG Signal Classification for Hand Gesture Recognition](https://mlsanity.com/paper/2411.15655/) (2024)
- [AnchorInv: Few-Shot Class-Incremental Learning of Physiological Signals via Representation Space Guided Inversion](https://mlsanity.com/paper/2412.13714/) (2024)
- [Time Frequency Analysis of EMG Signal for Gesture Recognition using Fine grained Features](https://mlsanity.com/paper/2504.14708/) (2025)
- [SASG-DA: Sparse-Aware Semantic-Guided Diffusion Augmentation For Myoelectric Gesture Recognition](https://mlsanity.com/paper/2511.08344/) (2025)
- [Modality vs. Morphology: A Framework for Time Series Classification for Biological Signals](https://mlsanity.com/paper/2605.18483/) (2026)
- [Translating Signals to Languages for sEMG-Based Activity Recognition](https://mlsanity.com/paper/2605.22403/) (2026)
- [Combining Statistical Features and Deep Encodings for Rehearsal-Based Class-Incremental Time Series Classification](https://mlsanity.com/paper/2606.03292/) (2026)
- [MyoCodec: A Streaming Neural Codec for Electromyography](https://mlsanity.com/paper/2609.36687/) (2026)
- [LiteEMG-FM: An Efficient and Deployable Foundation Model for Robust EMG Sensing](https://mlsanity.com/paper/2610.02497/) (2026)

## Abstract (arXiv)

Recently, surface electromyogram (EMG) has been proposed as a novel biometric trait for addressing some key limitations of current biometrics, such as spoofing and liveness. The EMG signals possess a unique characteristic: they are inherently different for individuals (biometrics), and they can be customized to realize multi-length codes or passwords (for example, by performing different gestures). However, current EMG-based biometric research has two critical limitations: 1) a small subject pool, compared to other more established biometric traits, and 2) limited to single-session or single-day data sets. In this study, forearm and wrist EMG data were collected from 43 participants over three different days with long separation while they performed static hand and wrist gestures. The multi-day biometric authentication resulted in a median EER of 0.017 for the forearm setup and 0.025 for the wrist setup, comparable to well-established biometric traits suggesting consistent performance over multiple days. The presented large-sample multi-day data set and findings could facilitate further research on EMG-based biometrics and other gesture recognition-based applications.