# Person Identification and Body Mass Index: A Deep Learning-Based Study on Micro-Dopplers

arXiv: https://arxiv.org/abs/1811.07173 · HTML: https://mlsanity.com/paper/1811.07173/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2018.

## Built on by (most cited first, 4 of 4)

- [SLPC: a VRNN-based approach for stochastic lidar prediction and completion in autonomous driving](https://mlsanity.com/paper/2102.09883/) (2021)
- [A MIMO Radar-based Few-Shot Learning Approach for Human-ID](https://mlsanity.com/paper/2110.08595/) (2021)
- [A Multi-Characteristic Learning Method with Micro-Doppler Signatures for Pedestrian Identification](https://mlsanity.com/paper/2203.12236/) (2022)
- [Pedestrian Recognition with Radar Data-Enhanced Deep Learning Approach Based on Micro-Doppler Signatures](https://mlsanity.com/paper/2306.08303/) (2023)

## Abstract (arXiv)

Obtaining a smart surveillance requires a sensing system that can capture accurate and detailed information for the human walking style. The radar micro-Doppler (boldsymbolμ-D) analysis is proved to be a reliable metric for studying human locomotions. Thus, boldsymbolμ-D signatures can be used to identify humans based on their walking styles. Additionally, the signatures contain information about the radar cross section (RCS) of the moving subject. This paper investigates the effect of human body characteristics on human identification based on their boldsymbolμ-D signatures. In our proposed experimental setup, a treadmill is used to collect boldsymbolμ-D signatures of 22 subjects with different genders and body characteristics. Convolutional autoencoders (CAE) are then used to extract the latent space representation from the boldsymbolμ-D signatures. It is then interpreted in two dimensions using t-distributed stochastic neighbor embedding (t-SNE). Our study shows that the body mass index (BMI) has a correlation with the boldsymbolμ-D signature of the walking subject. A 50-layer deep residual network is then trained to identify the walking subject based on the boldsymbolμ-D signature. We achieve an accuracy of 98% on the test set with high signal-to-noise-ratio (SNR) and 84% in case of different SNR levels.