Analyzing features learned for Offline Signature Verification using Deep CNNs

Abstract

Research on Offline Handwritten Signature Verification explored a large variety of handcrafted feature extractors, ranging from graphology, texture descriptors to interest points. In spite of advancements in the last decades, performance of such systems is still far from optimal when we test the systems against skilled forgeries - signature forgeries that target a particular individual. In previous research, we proposed a formulation of the problem to learn features from data (signature images) in a Writer-Independent format, using Deep Convolutional Neural Networks (CNNs), seeking to improve performance on the task. In this research, we push further the performance of such method, exploring a range of architectures, and obtaining a large improvement in state-of-the-art performance on the GPDS dataset, the largest publicly available dataset on the task. In the GPDS-160 dataset, we obtained an Equal Error Rate of 2.74%, compared to 6.97% in the best result published in literature (that used a combination of multiple classifiers). We also present a visual analysis of the feature space learned by the model, and an analysis of the errors made by the classifier. Our analysis shows that the model is very effective in separating signatures that have a different global appearance, while being particularly vulnerable to forgeries that very closely resemble genuine signatures, even if their line quality is bad, which is the case of slowly-traced forgeries.

Accepted as a conference paper to ICPR 2016

References in corpus (4)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Offline Handwritten Signature Verification - Literature Review
• Writer-independent Feature Learning for Offline Signature Verification using Deep Convolutional Neural Networks

Cited by in corpus (7)

• Learning Features for Offline Handwritten Signature Verification using Deep Convolutional Neural Networks
• Offline Handwritten Signature Verification - Literature Review
• On Usage of Autoencoders and Siamese Networks for Online Handwritten Signature Verification
• Fixed-sized representation learning from Offline Handwritten Signatures of different sizes
• Multi-Representational Learning for Offline Signature Verification using Multi-Loss Snapshot Ensemble of CNNs
• Signature Verification using Geometrical Features and Artificial Neural Network Classifier
• A comprehensive study of sparse representation techniques for offline signature verification