# Beyond Sub-6 GHz: Leveraging mmWave Wi-Fi for Gait-Based Person Identification

arXiv: https://arxiv.org/abs/2510.08160 · HTML: https://mlsanity.com/paper/2510.08160/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Builds on (most cited first, 7 of 7)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [Deep Learning for Iris Recognition: A Survey](https://mlsanity.com/paper/2210.05866/) (2022)
- [Joint Activity Recognition and Indoor Localization with WiFi Fingerprints](https://mlsanity.com/paper/1904.04964/) (2019)
- [RAPID: Retrofitting IEEE 802.11ay Access Points for Indoor Human Detection and Sensing](https://mlsanity.com/paper/2109.04819/) (2021)
- [Gesture Recognition with mmWave Wi-Fi Access Points: Lessons Learned](https://mlsanity.com/paper/2306.17062/) (2023)

## Abstract (arXiv)

Person identification plays a vital role in enabling intelligent, personalized, and secure human-computer interaction. Recent research has demonstrated the feasibility of leveraging Wi-Fi signals for passive person identification using a person's unique gait pattern. Although most existing work focuses on sub-6 GHz frequencies, the emergence of mmWave offers new opportunities through its finer spatial resolution, though its comparative advantages for person identification remain unexplored. This work presents the first comparative study between sub-6 GHz and mmWave Wi-Fi signals for person identification with commercial off-the-shelf (COTS) Wi-Fi, using a novel dataset of synchronized measurements from the two frequency bands in an indoor environment. To ensure a fair comparison, we apply identical training pipelines and model configurations across both frequency bands. Leveraging end-to-end deep learning, we show that even at low sampling rates (10 Hz), mmWave Wi-Fi signals can achieve high identification accuracy (91.2% on 20 individuals) when combined with effective background subtraction.