Wearables-Driven Freeform Handwriting Authentication

Abstract

With the ubiquity of handwriting in everyday tasks, it is surprising that existing avenues for handwriting authentication remain largely out of reach for the average individual or organization. Current solutions often rely on expensive or specialized equipment, and most existing research focuses on signatures rather than freeform handwriting. This limits the applicability of such technology to a narrow range of scenarios. In this paper, we argue that wearable devices might make handwriting authentication scalable and affordable. We design and evaluate two wearables-driven freeform handwriting authentication systems, one centered on a deep neural network and the other using human-engineered features. Our authentication systems are thoroughly tested across three writing experiments (involving 53 participants) that were carefully mapped to typical writing scenarios. We show the best performing configuration to attain an equal error rate of 5.51%, suggesting the potential of this modality for use in a multi-modal authentication system. To evaluate how our authentication systems perform against attacks by determined attackers, we developed and evaluated two impostor attacks that correspond to highly likely attack vectors. We then show that certain authentication system configurations are resistant to the attack. This paper represents an important step toward consumer ready wearables-driven freeform handwriting authentication.