# Exploring Transformers for Behavioural Biometrics: A Case Study in Gait Recognition

arXiv: https://arxiv.org/abs/2206.01441 · HTML: https://mlsanity.com/paper/2206.01441/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 46 references that are in the map.
- First posted on arXiv 3 Jun 2022.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [Generating Long Sequences with Sparse Transformers](https://mlsanity.com/paper/1904.10509/) (2019)
- [Efficient Transformers: A Survey](https://mlsanity.com/paper/2009.06732/) (2020)
- [Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting](https://mlsanity.com/paper/1907.00235/) (2019)
- [Transformers in Time Series: A Survey](https://mlsanity.com/paper/2202.07125/) (2022)
- [Block-Recurrent Transformers](https://mlsanity.com/paper/2203.07852/) (2022)
- [Deep Gait Recognition: A Survey](https://mlsanity.com/paper/2102.09546/) (2021)
- [DeepWriteSYN: On-Line Handwriting Synthesis via Deep Short-Term Representations](https://mlsanity.com/paper/2009.06308/) (2020)
- [DeepSign: Deep On-Line Signature Verification](https://mlsanity.com/paper/2002.10119/) (2020)
- [Gait Recognition Based on Deep Learning: A Survey](https://mlsanity.com/paper/2201.03323/) (2022)
- [Deep Learning-Based Gait Recognition Using Smartphones in the Wild](https://mlsanity.com/paper/1811.00338/) (2018)
- [BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks](https://mlsanity.com/paper/2001.10223/) (2020)
- [A Survey of Privacy Vulnerabilities of Mobile Device Sensors](https://mlsanity.com/paper/2106.10154/) (2021)
- [SVC-onGoing: Signature Verification Competition](https://mlsanity.com/paper/2108.06090/) (2021)
- [ECG Biometric Recognition: Review, System Proposal, and Benchmark Evaluation](https://mlsanity.com/paper/2204.03992/) (2022)
- [IDNet: Smartphone-based Gait Recognition with Convolutional Neural Networks](https://mlsanity.com/paper/1606.03238/) (2016)
- [GaitPrivacyON: Privacy-Preserving Mobile Gait Biometrics using Unsupervised Learning](https://mlsanity.com/paper/2110.03967/) (2021)
- [DT-SV: A Transformer-based Time-domain Approach for Speaker Verification](https://mlsanity.com/paper/2205.13249/) (2022)

## Built on by (most cited first, 13 of 13)

- [How Good is ChatGPT at Face Biometrics? A First Look into Recognition, Soft Biometrics, and Explainability](https://mlsanity.com/paper/2401.13641/) (2024)
- [AFR-Net: Attention-Driven Fingerprint Recognition Network](https://mlsanity.com/paper/2211.13897/) (2022)
- [TypeFormer: Transformers for Mobile Keystroke Biometrics](https://mlsanity.com/paper/2212.13075/) (2022)
- [IJCB 2022 Mobile Behavioral Biometrics Competition (MobileB2C)](https://mlsanity.com/paper/2210.03072/) (2022)
- [Keystroke Verification Challenge (KVC): Biometric and Fairness Benchmark Evaluation](https://mlsanity.com/paper/2311.06000/) (2023)
- [Mobile Contactless Palmprint Recognition: Use of Multiscale, Multimodel Embeddings](https://mlsanity.com/paper/2401.08111/) (2024)
- [IEEE BigData 2023 Keystroke Verification Challenge (KVC)](https://mlsanity.com/paper/2401.16559/) (2024)
- [KVC-onGoing: Keystroke Verification Challenge](https://mlsanity.com/paper/2412.20530/) (2024)
- [Is It Really You? Exploring Biometric Verification Scenarios in Photorealistic Talking-Head Avatar Videos](https://mlsanity.com/paper/2508.00748/) (2025)
- [Exploring Transformers for On-Line Handwritten Signature Verification](https://mlsanity.com/paper/2307.01663/) (2023)
- [Leveraging Avatar Fingerprinting: A Multi-Generator Photorealistic Talking-Head Public Database and Benchmark](https://mlsanity.com/paper/2603.26934/) (2026)
- [CareFall: Automatic Fall Detection through Wearable Devices and AI Methods](https://mlsanity.com/paper/2307.05275/) (2023)
- [Exploring Vision-Language Models for Online Signature Verification: A Zero-Shot Capability Study](https://mlsanity.com/paper/2605.14845/) (2026)

## Abstract (arXiv)

Biometrics on mobile devices has attracted a lot of attention in recent years as it is considered a user-friendly authentication method. This interest has also been motivated by the success of Deep Learning (DL). Architectures based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been established to be convenient for the task, improving the performance and robustness in comparison to traditional machine learning techniques. However, some aspects must still be revisited and improved. To the best of our knowledge, this is the first article that intends to explore and propose novel gait biometric recognition systems based on Transformers, which currently obtain state-of-the-art performance in many applications. Several state-of-the-art architectures (Vanilla, Informer, Autoformer, Block-Recurrent Transformer, and THAT) are considered in the experimental framework. In addition, new configurations of the Transformers are proposed to further increase the performance. Experiments are carried out using the two popular public databases whuGAIT and OU-ISIR. The results achieved prove the high ability of the proposed Transformer, outperforming state-of-the-art CNN and RNN architectures.