Gait-Watch

Abstract

With recent advances in mobile computing and sensing technology, smart wearable devices have pervaded our everyday lives. The security of these wearable devices is becoming a hot research topic because they store various private information. Existing approaches either only rely on a secret PIN number or require an explicit user authentication process. In this paper, we present Gait-watch, a context-aware authentication system for smart watch based on gait recognition. We address the problem of recognizing the user under various walking activities (e.g., walking normally, walking with calling the phone), and propose a sparse fusion method to improve recognition accuracy. Extensive evaluations show that Gait-watch improves recognition accuracy by up to 20% by leveraging the activity information, and the proposed sparse fusion method is 10% better than several state-of-the-art gait recognition methods. We also report a user study to demonstrate that Gait-watch can accurately authenticate the user in real world scenarios and require low system cost.