Offline Signature Verification with AutoencoderCNN Hybrid Feature Extraction for Improved Fake Signature Detection

A novel approach aimed at elevating the performance of offline signature verification systems by harnessing the combined power of Autoencoders and Convolutional Neural Networks (CNNs). Our evaluation encompassed Convolutional Neural Networks (CNN), K-Nearest Neighbours (KNN), Support Vector Machines (SVM), Gaussian Temporal Rule, Probabilistic Neural Networks, Multi-layered Perception, Long Short-Term Memory (LSTM), and various combinations thereof. Notably, our proposed Autoencoder with Convolutional Neural Networks (AE with CNN) outshone all other approaches, achieving an impressive accuracy rate of $98.48 \%$. While CNN displayed commendable performance at $89 \%$, KNN and SVM fusion attained $78.50 \%$ accuracy, suggesting room for improvement in distinguishing genuine and forged signatures. The Gaussian Temporal Rule proved robust, with an accuracy of $91.20 \%$, and Probabilistic Neural Networks and Multi-layered Perception with SVM reached accuracies of $92.06 \%$ and $91.67 \%$, respectively The introduction of LSTM in conjunction with SVM and KNN significantly enhanced accuracy to $95.40 \%, 95.20 \%$, and $92.70 \%$, respectively. Collectively, these findings provide valuable insights into the potential of AE with CNN as a leading solution for achieving highly accurate signature verification, particularly in contexts where the distinction between authentic and counterfeit signatures is critical.