This paper introduces a novel approach to leverage the knowledge of existing expert models for training new Convolutional Neural Networks, on domains where task-specific data are limited or unavailable. The presented scheme is applied in offline handwritten signature verification (OffSV) which, akin to other biometric applications, suffers from inherent data limitations due to regulatory restrictions. The proposed Student-Teacher (S-T) configuration utilizes feature-based knowledge distillation (FKD), combining graph-based similarity for local activations with global similarity measures to supervise student's training, using only handwritten text data. Remarkably, the models trained using this technique exhibit comparable, if not superior, performance to the teacher model across three popular signature datasets. More importantly, these results are attained without employing any signatures during the feature extraction training process. This study demonstrates the efficacy of leveraging existing expert models to overcome data scarcity challenges in OffSV and potentially other related domains.

翻译：本文提出了一种新颖方法，利用现有专家模型的知识，在特定任务数据有限或不可用的领域中训练新的卷积神经网络。该方案应用于离线手写签名验证（OffSV），该领域与其他生物特征应用类似，由于监管限制而面临固有的数据局限性。所提出的学生-教师（S-T）配置采用基于特征的知识蒸馏（FKD），结合基于图的局部激活相似性度量与全局相似性度量，仅使用手写文本数据监督学生模型的训练。值得注意的是，使用该技术训练的模型在三个主流签名数据集上展现出与教师模型相当甚至更优的性能。更重要的是，这些结果是在特征提取训练过程中未使用任何签名数据的情况下实现的。本研究证明了利用现有专家模型克服离线签名验证及其他相关领域数据稀缺挑战的有效性。