Trusted Worrier: A low-cost and high-accuracy user authentication system for firearm exploiting dynamic hand pressure biometrics

Usage of firearm by only original users is one of the prime concerns of the research community considering limitless damage and even lethal consequences in case of having the usage in any other way. However, a low-cost, limited-resources, and high-accuracy solution for performing real-time user identification of firearm is yet to be proposed in the literature. As a remedy to this situation, in this paper, we propose a novel solution named Trusted Worrier that can identify users of a firearm in real time using only a small number of low-cost and low-power COTS pressure sensors. Here, we propose judicious positioning of the sensors such that the number of required sensors can retain a small value (five in our case). Besides, we develop a novel machine learning technique that exhibits high accuracy in user authentication demanding small amount of resource and execution time. We evaluate the approach using real data collected from twenty nine users. Our rigorous analysis over the data confirms effectiveness of Trusted Worrier in identifying users of a firearm.