# Multi Sensor-based Implicit User Identification

## TL;DR

This paper presents a multi-sensor gait biometric system for automatic user identification on smartphones, achieving high accuracy and robustness in indoor environments to enhance security and usability.

## Contribution

It introduces a novel gait-based biometric identification method using multi-sensor data, optimized feature selection, and multiple classifiers, demonstrating high accuracy in real-world indoor tests.

## Key findings

- KNN and bagging classifiers achieve 87-99% accuracy.
- The system attains 100% true positive rate and 0% false-negative rate.
- Effective in identifying users with minimal samples per window.

## Abstract

Smartphones have ubiquitously integrated into our home and work environments, however, users normally rely on explicit but inefficient identification processes in a controlled environment. Therefore, when a device is stolen, a thief can have access to the owner's personal information and services against the stored passwords. As a result of this potential scenario, this work proposes an automatic legitimate user identification system based on gait biometrics extracted from user walking patterns captured by a smartphone. A set of preprocessing schemes is applied to calibrate noisy and invalid samples and augment the gait-induced time and frequency domain features, then further optimized using a non-linear unsupervised feature selection method. The selected features create an underlying gait biometric representation able to discriminate among individuals and identify them uniquely. Different classifiers (i.e. Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Bagging, and Extreme Learning Machine (ELM)) are adopted to achieve accurate legitimate user identification. Extensive experiments on a group of $16$ individuals in an indoor environment show the effectiveness of the proposed solution: with $5$ to $70$ samples per window, KNN and bagging classifiers achieve $87-99\%$ accuracy, $82-98\%$ for ELM, and $81-94\%$ for SVM. The proposed pipeline achieves a $100\%$ true positive and $0\%$ false-negative rate for almost all classifiers.

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Source: https://tomesphere.com/paper/1706.01739