Recurrent Adaptation Networks for Online Signature Verification

Online signature verification remains a challenging task owing to large intra-individual variability. To tackle this problem, in this paper, we propose to use recurrent neural networks (RNN) for representation learning in the dynamic time warping framework. Metric-based loss functions are designed explicitly to minimize intra-individual variability and enhance inter-individual variability and to guide the RNN in learning discriminative representations for online signatures. An RNN variant—gated auto regressive units—is proposed and shows a better generalization performance in our framework. Furthermore, we interpret the online signature verification problem as a meta-learning problem: one client is considered as one task, therefore, different clients compose the task space. Based on this formulation, we design an end-to-end trainable meta-layer that learns to adapt to different clients, allowing fast adaptation to new clients in the test stage. In addition, a new descriptor—the length-normalized path signature—is proposed to describe online signatures. Our proposed system achieves a state-of-the-art performance on three benchmark datasets, namely, MCYT-100, Mobisig, and e-BioSign.