Symbolic data analysis-based few-shot learning for offline handwritten signature verification
Mohamed Anis Djoudjai, Youcef Chibani, Adel Hafiane · International Journal of Biometrics · 2025
This paper presents a novel approach for offline handwritten signature verification using few-shot learning and symbolic data analysis. The method effectively handles high intra-class variability and limited data availability, common challenges in signature recognition. The model is trained on dissimilarities from the Signet feature extractor, capturing subtle differences within the same writer's signatures. A new weighted membership function measures similarity between query and reference signatures. The method outperforms traditional approaches, achieving competitive equal error rates on four public datasets (GPDS, CEDAR, MCYT, PUC-PR) using only five genuine reference signatures. The system surpasses state-of-the-art results on GPDS and PUC-PR datasets. This combination of few-shot learning and symbolic data analysis offers robust and efficient signature verification, ideal for real-world applications with scarce labelled data.