Frank Sicong Chen | Searcharxiv

arXiv2026

VIGIL: Verifying Identity via Gated Intermittent Likelihoods for Continuous Biometric Authentication

Continuous multi-modal authentication has emerged as a necessity for securing modern environments against persistent threats. Existing temporal fusion techniques fail to identify a persistent attacker from a genuine user with poor signal strength. In this study, we propose VIGIL (Verifying Identity via Gated Intermittent Likelihoods for Continuous Biometric Authentication), a highly adaptive continuous authentication framework. We introduce configurable cross-modal fusion with per-modality weighting, enabling operators to select their choice of integration strategy. We improve temporal fusion using dual-state State Transition Machines (STM) with unidirectional transition matrices. A three-zone verification decision model that enables multi-round verification when evidence is inconclusive is used in combination with an adaptive shrinking verification window. Monotonic decay, backflow elimination and analytical evaluation demonstrate that the proposed framework effectively addresses the limitations of existing approaches and reduces the time to detect intrusions while maintaining high usability for legitimate users.

cs.CR↗

arXiv2022

Formalizing PQRST Complex in Accelerometer-based Gait Cycle for Authentication

Accelerometer signals generated through gait present a new frontier of human interface with mobile devices. Gait cycle detection based on these signals has applications in various areas, including authentication, health monitoring, and activity detection. Template-based studies focus on how the entire gait cycle represents walking patterns, but these are compute-intensive. Aggregate feature-based studies extract features in the time domain and frequency domain from the entire gait cycle to reduce the number of features. However, these methods may miss critical structural information needed to appropriately represent the intricacies of walking patterns. To the best of our knowledge, no study has formally proposed a structure to capture variations within gait cycles or phases from accelerometer readings. We propose a new structure named the PQRST Complex, which corresponds to the swing phase in a gait cycle and matches the foot movements during this phase, thus capturing the changes in foot position. In our experiments, based on the nine features derived from this structure, the accelerometer-based gait authentication system outperforms many state-of-the-art gait cycle-based authentication systems. Our work opens up a new paradigm of capturing the structure of gait and opens multiple areas of research and practice using gait analogous to the "QRS complex" structure of ECG signals related to the heart.

cs.HC↗