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.

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.