[Paper Review] Leveraging Expert Models for Training Deep Neural Networks in Scarce Data Domains: Application to Offline Handwritten Signature Verification

Dimitrios Tsourounis, Ilias Theodorakopoulos+2 more|arXiv (Cornell University)|Aug 2, 2023

Handwritten Text Recognition TechniquesComputer Science3 citations

TL;DR

This paper proposes a student-teacher knowledge distillation framework that leverages pre-trained expert models to train deep neural networks for offline handwritten signature verification in data-scarce environments. By using only handwritten text data to supervise feature-based knowledge distillation—combining graph-based local and global similarity measures—models achieve performance on par with or better than the teacher, without requiring any signature data during feature extraction training.

ABSTRACT

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.

Motivation & Objective

• Address the challenge of data scarcity in offline handwritten signature verification (OffSV), where regulatory and privacy constraints limit access to real signature data.
• Overcome the limitations of existing deep learning models in OffSV that require large-scale, task-specific datasets for effective training.
• Develop a knowledge distillation framework that transfers knowledge from expert models to student networks without using signature data during feature extraction.
• Demonstrate that performance comparable to or exceeding the teacher model can be achieved using only text-based data for supervision.
• Enable transfer learning in low-data regimes by leveraging pre-trained models trained on unrelated but accessible data (e.g., text images).

Proposed method

• Adopt a Student-Teacher (S-T) training configuration where the teacher is a pre-trained expert model on a related but distinct task (e.g., text recognition).
• Apply feature-based knowledge distillation (FKD) to transfer knowledge from the teacher’s internal activations to the student network.
• Use graph-based similarity to measure local feature correspondence between teacher and student activations across spatial locations.
• Combine local graph-based similarity with global similarity metrics (e.g., cosine similarity of pooled features) to supervise the student’s training.
• Train the student network exclusively on handwritten text data, avoiding any use of signature images during the feature extraction phase.
• Optimize the student using a multi-component loss that balances feature-level and global similarity to ensure robust representation learning.

Experimental results

Research questions

• RQ1 Can knowledge distillation from expert models trained on unrelated data (e.g., text) effectively improve performance in low-data OffSV tasks?
• RQ2 To what extent can a student network achieve performance comparable to the teacher model when trained without any signature data during feature learning?
• RQ3 How effective is the combination of graph-based local similarity and global similarity measures in guiding student network training in data-scarce settings?
• RQ4 Does using only text data for distillation supervision lead to generalizable representations in signature verification despite domain shift?
• RQ5 Can this approach be generalized to other biometric or document analysis tasks suffering from data scarcity?

Key findings

• The student model achieves performance on three standard OffSV datasets that matches or exceeds that of the teacher model, despite no access to signature data during training.
• The method demonstrates strong generalization, with the student network outperforming the teacher on at least one of the three benchmark datasets.
• The use of graph-based local similarity in conjunction with global similarity measures significantly improves feature alignment and model robustness.
• Performance gains are achieved without fine-tuning on signature data, highlighting the effectiveness of knowledge transfer from unrelated but accessible data sources.
• The approach enables high-performance OffSV models even in the absence of large-scale signature datasets, making it viable for privacy-constrained environments.
• The results validate that pre-trained expert models can serve as powerful knowledge sources in low-data regimes, especially when data collection is restricted.