# Learning Human Identity from Motion Patterns

arXiv: https://arxiv.org/abs/1511.03908 · HTML: https://mlsanity.com/paper/1511.03908/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2015.

## Built on by (most cited first, 9 of 9)

- [BehavePassDB: Public Database for Mobile Behavioral Biometrics and Benchmark Evaluation](https://mlsanity.com/paper/2206.02502/) (2022)
- [IJCB 2022 Mobile Behavioral Biometrics Competition (MobileB2C)](https://mlsanity.com/paper/2210.03072/) (2022)
- [Mobile Behavioral Biometrics for Passive Authentication](https://mlsanity.com/paper/2203.07300/) (2022)
- [Careful with That! Observation of Human Movements to Estimate Objects Properties](https://mlsanity.com/paper/2103.01555/) (2021)
- [HARMES: A Multi-Modal Dataset for Wearable Human Activity Recognition with Motion, Environmental Sensing and Sound](https://mlsanity.com/paper/2605.02596/) (2026)
- [From Volume to Value: Preference-Aligned Memory Construction for On-Device RAG](https://mlsanity.com/paper/2605.18271/) (2026)
- [Improving the Authentication with Built-in Camera Protocol Using Built-in Motion Sensors: A Deep Learning Solution](https://mlsanity.com/paper/2107.10536/) (2021)
- [Motion ID: Human Authentication Approach](https://mlsanity.com/paper/2302.01751/) (2023)
- [Improving the Security of Smartwatch Payment with Deep Learning](https://mlsanity.com/paper/2307.05437/) (2023)

## Abstract (arXiv)

We present a large-scale study exploring the capability of temporal deep neural networks to interpret natural human kinematics and introduce the first method for active biometric authentication with mobile inertial sensors. At Google, we have created a first-of-its-kind dataset of human movements, passively collected by 1500 volunteers using their smartphones daily over several months. We (1) compare several neural architectures for efficient learning of temporal multi-modal data representations, (2) propose an optimized shift-invariant dense convolutional mechanism (DCWRNN), and (3) incorporate the discriminatively-trained dynamic features in a probabilistic generative framework taking into account temporal characteristics. Our results demonstrate that human kinematics convey important information about user identity and can serve as a valuable component of multi-modal authentication systems.