Cross-2D-to-3D　(2D-3D)　heterogeneous　palmprint　recognition　aims　to　matching　a　2D　palmprint　probe　with　the　3D　palmprint　galleries,　which　shows　great　potential　for　biometric　applications　due　to　rich　information　of　3D　images　and　the　low-cost　of　2D　image　acquisition.　However,　the　large　structural　discrepancy　between　2D　and　3D　palmprint　images　makes　it　hard　to　directly　conduct　matching　between　2D-3D　heterogeneous　palmprint　images.　In　this　paper,　we　propose　a　palmprint　image　generative　adversarial　network　(PIGAN)　to　convert　2D　palmprint　images　into　3D　domain　for　2D-3D　heterogeneous　palmprint　recognition.　We　first　calculate　the　mean　curvature　images　(MCIs)　to　present　the　3D　palm　surface　measurements　of　3D　palmprint　images.　Then,　we　employ　an　image-to-image　generation　backbone　to　transform　2D　palmprint　images　into　3D　MCI　representations.　To　make　the　fake　MCIs　realistic,　we　impose　both　adversarial　and　identity-aware　learning　losses　to　protect　the　discriminative　information　of　MCIs,　and　further　introduce　a　visual-recovery　loss　to　restore　the　visual-specific　texture　characteristics　of　palmprints.　By　this　way,　high-quality　3D　palmprint　MCI　can　be　synthesized　with　high　similarity　as　the　real　ones　at　both　feature　and　visual　levels,　such　that　the　pixel-level　gap　between　2D　and　3D　palmprint　images　can　be　effectively　reduced　for　2D-3D　heterogeneous　palmprint　recognition.　Extensive　experimental　results　on　the　widely-used　PolyU　2D-3D　palmprint　database　clearly　show　the　effectiveness　of　the　proposed　PIGAN　in　improving　the　performance　of　2D-3D　heterogeneous　palmprint　recognition.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.