# IDNet: Smartphone-based Gait Recognition with Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1606.03238 · HTML: https://mlsanity.com/paper/1606.03238/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2016.

## Built on by (most cited first, 10 of 10)

- [Meta-HAR: Federated Representation Learning for Human Activity Recognition](https://mlsanity.com/paper/2106.00615/) (2021)
- [Exploring Transformers for Behavioural Biometrics: A Case Study in Gait Recognition](https://mlsanity.com/paper/2206.01441/) (2022)
- [GaitPrivacyON: Privacy-Preserving Mobile Gait Biometrics using Unsupervised Learning](https://mlsanity.com/paper/2110.03967/) (2021)
- [Interpretable Deep Learning for the Remote Characterisation of Ambulation in Multiple Sclerosis using Smartphones](https://mlsanity.com/paper/2103.09171/) (2021)
- [Similarity Embedding Networks for Robust Human Activity Recognition](https://mlsanity.com/paper/2106.15283/) (2021)
- [Binarized Neural Networks for Resource-Constrained On-Device Gait Identification](https://mlsanity.com/paper/2103.16609/) (2021)
- [Security and Privacy Enhanced Gait Authentication with Random Representation Learning and Digital Lockers](https://mlsanity.com/paper/2108.02400/) (2021)
- [Spatio-temporal Gait Feature with Global Distance Alignment](https://mlsanity.com/paper/2203.03376/) (2022)
- [Leveraging Smartphone Sensors for Detecting Abnormal Gait for Smart Wearable Mobile Technologies](https://mlsanity.com/paper/2208.01876/) (2022)
- [Non-Contrastive Learning-based Behavioural Biometrics for Smart IoT Devices](https://mlsanity.com/paper/2210.12964/) (2022)

## Abstract (arXiv)

Here, we present IDNet, a user authentication framework from smartphone-acquired motion signals. Its goal is to recognize a target user from their way of walking, using the accelerometer and gyroscope (inertial) signals provided by a commercial smartphone worn in the front pocket of the user's trousers. IDNet features several innovations including: i) a robust and smartphone-orientation-independent walking cycle extraction block, ii) a novel feature extractor based on convolutional neural networks, iii) a one-class support vector machine to classify walking cycles, and the coherent integration of these into iv) a multi-stage authentication technique. IDNet is the first system that exploits a deep learning approach as universal feature extractors for gait recognition, and that combines classification results from subsequent walking cycles into a multi-stage decision making framework. Experimental results show the superiority of our approach against state-of-the-art techniques, leading to misclassification rates (either false negatives or positives) smaller than 0.15% with fewer than five walking cycles. Design choices are discussed and motivated throughout, assessing their impact on the user authentication performance.