IFViT: Interpretable Fixed-Length Representation for Fingerprint Matching via Vision Transformer

arXiv:2404.08237v1 Announce Type: new
Abstract: Determining dense feature points on fingerprints used in constructing deep fixed-length representations for accurate matching, particularly at the pixel level, is of significant interest. To explore the interpretability of fingerprint matching, we propose a multi-stage interpretable fingerprint matching network, namely Interpretable Fixed-length Representation for Fingerprint Matching via Vision Transformer (IFViT), which consists of two primary modules. The first module, an interpretable dense registration module, establishes a Vision Transformer (ViT)-based Siamese Network to capture long-range dependencies …

abstract arxiv cs.ai cs.cv explore feature fingerprints interpretability network pixel representation stage transformer type via vision