Gait Data Synthesis for Smartphone User Authentication

Gait, the distinctive and natural pattern of an individual's walking behavior, has emerged as an effective biometric for user authentication. Its ability to operate without active user involvement, along with its resistance to replication, makes it particularly well‐suited for continuous smartphone authentication. However, real‐world deployment remains challenging due to diverse user activities and varying environmental conditions. Therefore, this study proposes a data synthesis approach to enhance the robustness and generalization of gait‐based authentication systems in real‐world environment. We introduce a Data Synthetic Model that combines a Variational Autoencoder (VAE) with a Bidirectional Long Short‐Term Memory (Bi‐LSTM) network. This architecture generates synthetic gait sequences that are temporally consistent, reflecting real‐world variability while maintaining essential gait dynamics. Evaluations on the ETRI Lifelog dataset demonstrate that the proposed method enhances performance of the smartphone authentication system, improving accuracy from 0.91 to 0.951 and reducing the Equal Error Rate (EER) from 0.101 to 0.040. These results highlight the potential of data synthesis for advancing real‐world gait authentication. While further improvements are needed for deployment in high‐security applications, this study provides a strong foundation for developing robust and scalable gait‐based biometric systems.