Dynamic time warping (DTW) is a popular technique for sequence alignment, and is the de facto standard for dynamic signature verification. In this paper, we go a significant step further to enhance DTW with the capability of deep representation learning, and propose an end-to-end trainable Deep soft-DTW (DsDTW) model for dynamic signature verification. Specifically, we design a convolutional recurrent adaptive network (CRAN) to process dynamic signatures, and utilize it to provide robust and discriminative local representations as inputs for DTW. As DTW is not fully differentiable with regard to its inputs, we introduce its smoothed formulation, soft-DTW, and incorporate the soft-DTW distances of signature pairs into the loss function for optimization. Because soft-DTW is differentiable, the proposed DsDTW is end-to-end trainable, and achieves an elegant integration of CRAN deep learning model and traditional DTW mechanism. Our method achieves state-of-the-art performance on several public benchmarks, and has won first place in the ICDAR 2021 competition for online signature verification. Source codes of DsDTW is available at https://github.com/KAKAFEI123/DsDTW.

中文翻译：

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DsDTW：使用深度 soft-DTW 进行局部表示学习以进行动态签名验证


动态时间规整（DTW）是一种流行的序列比对技术，也是动态签名验证的事实上的标准。在本文中，我们在通过深度表示学习的能力增强 DTW 方面迈出了重要的一步，并提出了一种用于动态签名验证的端到端可训练的深度软 DTW（DsDTW）模型。具体来说，我们设计了一个卷积循环自适应网络（CRAN）来处理动态签名，并利用它来提供鲁棒且有辨别力的局部表示作为 DTW 的输入。由于 DTW 对于其输入并不是完全可微的，因此我们引入了其平滑公式 soft-DTW，并将签名对的 soft-DTW 距离合并到损失函数中进行优化。由于soft-DTW是可微的，因此所提出的DsDTW是端到端可训练的，并实现了CRAN深度学习模型和传统DTW机制的优雅集成。我们的方法在多个公共基准测试中实现了最先进的性能，并在 ICDAR 2021 在线签名验证竞赛中获得第一名。 DsDTW 的源代码可在 https://github.com/KAKAFEI123/DsDTW 获取。