The effect of fatigue on the performance of online writer recognition

TL;DR

This study investigates how physical fatigue influences intra-user variability and the accuracy of signature and text-based biometric recognition methods, finding that fatigue affects text recognition more than signature recognition.

Contribution

It provides a comparative analysis of the impact of fatigue on different biometric modalities and recognition techniques, highlighting the robustness of signature recognition under fatigue conditions.

Findings

Fatigue significantly affects text-based recognition performance.

Signature recognition remains stable despite fatigue.

Long sequences in text recognition mitigate fatigue effects.

Abstract

Background: The performance of biometric modalities based on things done by the subject, like signature and text-based recognition, may be affected by the subject state. Fatigue is one of the conditions that can significantly affect the outcome of handwriting tasks. Recent research has already shown that physical fatigue produces measurable differences in some features extracted from common writing and drawing tasks. It is important to establish to which extent physical fatigue contributes to the intra-person variability observed in these biometric modalities and also to know whether the performance of recognition methods is affected by fatigue. Goal: In this paper we assess the impact of fatigue on intra-user variability and on the performance of signature-based and text-based writer recognition approaches encompassing both identification and verification. Methods: Several signature and text recognition methods are considered and applied to samples gathered after different levels of induced fatigue, measured by metabolic and mechanical assessment and, also by subjective perception. The recognition methods are Dynamic Time Warping and Multi Section Vector Quantization, for signatures, and Allographic Text-Dependent Recognition for text in capital letters. For each fatigue level, the identification and verification performance of these methods is measured. Results: Signature shows no statistically significant intra-user impact, but text does. On the other hand, performance of signature-based recognition approaches is negatively impacted by fatigue whereas the impact is not noticeable in text-based recognition, provided long enough sequences are considered.