Beyond Sub-6 GHz: Leveraging mmWave Wi-Fi for Gait-Based Person Identification
It addresses person identification for human-computer interaction, but is incremental as it compares existing frequency bands without introducing a new method.
This paper tackles the problem of passive person identification by comparing sub-6 GHz and mmWave Wi-Fi signals for gait-based recognition, showing that mmWave achieves 91.2% accuracy on 20 individuals at low sampling rates.
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.