Offline Handwritten Signature verification presents a challenging Pattern Recognition problem, where only knowledge of the positive class is available for training. While classifiers have access to a few genuine signatures for training, during generalization they also need to discriminate forgeries. This is particularly challenging for skilled forgeries, where a forger practices imitating the user's signature, and often is able to create forgeries visually close to the original signatures. Most work in the literature address this issue by training for a surrogate objective: discriminating genuine signatures of a user and random forgeries (signatures from other users). In this work, we propose a solution for this problem based on meta-learning, where there are two levels of learning: a task-level (where a task is to learn a classifier for a given user) and a meta-level (learning across tasks). In particular, the meta-learner guides the adaptation (learning) of a classifier for each user, which is a lightweight operation that only requires genuine signatures. The meta-learning procedure learns what is common for the classification across different users. In a scenario where skilled forgeries from a subset of users are available, the meta-learner can guide classifiers to be discriminative of skilled forgeries even if the classifiers themselves do not use skilled forgeries for learning. Experiments conducted on the GPDS-960 dataset show improved performance compared to Writer-Independent systems, and achieve results comparable to state-of-the-art Writer-Dependent systems in the regime of few samples per user (5 reference signatures).

翻译：脱线手写签名校验是一个具有挑战性的模式识别问题, 只有对正类的知识才能用于培训。 虽然分类者能够获得一些真正的培训签名, 在一般化过程中他们也需要区别伪造。 对于熟练的伪造者来说,这尤其具有挑战性, 仿照用户签名的伪造者做法, 并且常常能够创建与原始签名相近的伪造。 文献中的大部分工作都通过替代目标培训来解决这一问题: 区分用户的真实签名和随机伪造( 来自其他用户的签名) 。 在这项工作中, 我们基于元学习提出这一问题的解决办法, 其中用户学习分为两个层次: 任务级别( 任务就是为特定用户学习一个分类者学习一个分类者) 和元级别( 跨任务学习)。 特别是, 元- 分类者指导每个用户的分类器的调整( 学习), 这是一种轻量的操作, 只需要真正的签名。 元学习程序可以了解不同用户分类的常见的参考方法。 在一个假设中, 熟练的伪造者从一个小类的用户的分类到一个可比较的分类的分类, 高级的分类的分类, 将无法进行G级的分类。