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　and　the　global　context　in　fingerprint　pairs.　It　provides　interpretable　dense　pixel-wise　correspondences　of　feature　points　for　fingerprint　alignment　and　enhances　the　interpretability　in　the　subsequent　matching　stage.　The　second　module　takes　into　account　both　local　and　global　representations　of　the　aligned　fingerprint　pair　to　achieve　an　interpretable　fixed-length　representation　extraction　and　matching.　It　employs　the　ViTs　trained　in　the　first　module　with　the　additional　fully　connected　layer　and　retrains　them　to　simultaneously　produce　the　discriminative　fixed-length　representation　and　interpretable　dense　pixel-wise　correspondences　of　feature　points.　Extensive　experimental　results　on　diverse　publicly　available　fingerprint　databases　demonstrate　that　the　proposed　framework　not　only　exhibits　superior　performance　on　dense　registration　and　matching　but　also　significantly　promotes　the　interpretability　in　deep　fixed-length　representations-based　fingerprint　matching.