期刊
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
卷 14, 期 6, 页码 1624-1637出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIFS.2018.2883152
关键词
Online signature verification; recurrent neural network; dynamic time warping; meta-learning; path signature
资金
- National Key Research and Development Program of China [2016YFB1001405]
- GD-NSF [2017A030312006]
- NSFC [61673182, 61771199]
- GDSTP [2017A010101027, 201607010227]
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.
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