ASSV

Abstract

As one kind of biological characteristics of people, handwritten signature has been widely used in the banking industry, government and education. Verifying handwritten signatures manually causes too much human cost, and its high probability of errors can threaten the property safety and even society stability. Therefore, the need for an automatic verification system is emphasized. This paper proposes a device-free on-line handwritten signature verification system ASSV, providing paper-based handwritten signature verification service. As far as we know, ASSV is the first system which uses the changes of acoustic signals to realize signature verification. ASSV differs from previous on-line signature verification work in two aspects: 1. It requires neither a special sensor-instrumented pen nor a tablet; 2. People do not need to wear a device such as a smartwatch on the dominant hand for hand tracking. Differing from previous acoustic-based sensing systems, ASSV uses a novel chord-based method to estimate phase-related changes caused by tiny actions. Then based on the estimation, frequency-domain features are extracted by a discrete cosine transform (DCT). Moreover, a deep convolutional neural network (CNN) model fed with distance matrices is designed to verify signatures. Extensive experiments show that ASSV is a robust, efficient and secure system achieving an AUC of 98.7% and an EER of 5.5% with a low latency.