Signature Verification

Signature verification, aiming to authenticate individuals based on their handwritten signatures, is a crucial area of research with applications in security and finance. Current research focuses on improving accuracy and robustness using various deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and variational autoencoders (VAEs), often incorporating techniques like feature disentanglement, self-supervised learning, and metric learning. These advancements address challenges such as intra-writer variability, inter-writer similarity, and sophisticated forgery techniques, leading to more secure and reliable authentication systems. The development of explainable models and the exploration of novel data acquisition methods, such as air signing, further enhance the field's practical impact and trustworthiness.