Adaptive Knowledge Distillation for Classification of Hand Images using Explainable Vision Transformers

TL;DR

This paper explores the use of explainable vision transformers for hand image classification, introducing adaptive knowledge distillation techniques that prevent catastrophic forgetting across different domains, with promising results for real-world biometric applications.

Contribution

It proposes novel adaptive distillation methods leveraging ViT explainability to improve cross-domain hand image classification performance.

Findings

ViT models outperform traditional methods in hand image classification

Explainability tools reveal meaningful internal representations of ViTs

Adaptive distillation prevents catastrophic forgetting across domains

Abstract

Assessing the forensic value of hand images involves the use of unique features and patterns present in an individual's hand. The human hand has distinct characteristics, such as the pattern of veins, fingerprints, and the geometry of the hand itself. This paper investigates the use of vision transformers (ViTs) for classification of hand images. We use explainability tools to explore the internal representations of ViTs and assess their impact on the model outputs. Utilizing the internal understanding of ViTs, we introduce distillation methods that allow a student model to adaptively extract knowledge from a teacher model while learning on data of a different domain to prevent catastrophic forgetting. Two publicly available hand image datasets are used to conduct a series of experiments to evaluate performance of the ViTs and our proposed adaptive distillation methods. The experimental results demonstrate that ViT models significantly outperform traditional machine learning methods and the internal states of ViTs are useful for explaining the model outputs in the classification task. By averting catastrophic forgetting, our distillation methods achieve excellent performance on data from both source and target domains, particularly when these two domains exhibit significant dissimilarity. The proposed approaches therefore can be developed and implemented effectively for real-world applications such as access control, identity verification, and authentication systems.