MultiGait: A Multi-Sensor Multi-Perspective Multi-Session Biometric Inference Benchmark and its Dataset

2026-09-01

AI Summary

为解决智能城市传感器的隐私风险研究缺乏合适数据集的问题，通过建立MultiGait多传感器、多视角、多会话步态数据集，并使用多种识别系统验证，揭示了被认为对隐私友好的传感器仍存在身份推断风险。

Abstract

A lack of suitable datasets has limited the research into the privacy risks of novel smart city sensors, such as thermal cameras, depth cameras, and lidar. Given the number of unsubstantiated privacy claims and their potential widespread deployment into many people's everyday life, understanding the privacy risks of these sensors -- in isolation and in like-for-like comparisons -- is crucial. With MultiGait, we collected the first multi-sensor, multi-perspective, multi-session gait-focused dataset, for the corresponding, and additional more far-reaching investigations. The dataset, validated with multiple state-of-the-art recognition systems, comprises various walking modes and annotated personal attributes for 199 individuals, to ensure the benefit for advanced studies including cross-sensor recognition and anonymization at the edge. MultiGait represents a foundation for rigorous privacy investigations, demonstrated through an extensive identity inference benchmark across eight sensors, four perspectives, and three recording sessions. Our benchmark incidentally reveals that sensors often assumed to be privacy-friendly do still entail considerable identity inference risks, while the poor cross-session generalization of existing methods underscores an important research gap.

Problem

• privacy risks
• smart city sensors
• multi-sensor
• multi-perspective
• multi-session

Innovation

• Multi-Sensor
• Privacy Risks
• Identity Inference
• Cross-Session Generalization
• Smart City Sensors