  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 $0.94 \pm 0.07$, $F_1$ score of
$0.93 \pm 0.08$, an equal error rate (EER) of about $0.06$ at a lower
confidence threshold of 0.52, which shows the promise of this work.
