2020/08/25 by William Cheung, Sudip Vhaduri, Cheung, William +1 · 1 citation
Computer Science · Social Sciences · #Advanced Malware Detection Techniques #FOS: Computer and information sciences #FOS: Electrical engineering #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Privacy, Security, and Data Protection #Signal Processing (eess.SP) #User Authentication and Security Systems #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2008.12145
openalex publication_date 2020/08/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
As market wearables are becoming popular with a range of services, including making financial transactions, accessing cars, etc. that they provide based on various private information of a user, security of this information is becoming very important. However, users are often flooded with PINs and passwords in this internet of things (IoT) world. Additionally, hard-biometric, such as facial or finger recognition, based authentications are not adaptable for market wearables due to their limited sensing and computation capabilities. Therefore, it is a time demand to develop a burden-free implicit authentication mechanism for wearables using the less-informative soft-biometric data that are easily obtainable from the market wearables. In this work, we present a context-dependent soft-biometric-based wearable authentication system utilizing the heart rate, gait, and breathing audio signals. From our detailed analysis, we find that a binary support vector machine (SVM) with radial basis function (RBF) kernel can achieve an average accuracy of 0.94 ± 0.07, F1 score of 0.93 ± 0.08, an equal error rate (EER) of about 0.06 at a lower confidence threshold of 0.52, which shows the promise of this work.