Binary-based　feature　representation　methods　have　received　increasing　attention　in　palmprint　recognition　due　to　their　high　efficiency　and　great　robustness　to　illumination　variation.　However,　most　of　them　are　hand-designed　descriptors　that　generally　require　much　prior　knowledge　in　their　design.　On　the　other　hand,　conventional　single-view　palmprint　recognition　approaches　have　difficulty　in　expressing　the　features　of　each　sample　strongly,　especially　low-quality　palmprint　images.　To　solve　these　problems,　in　this　paper,　we　propose　a　novel　discriminative　multiview　learning　method,　named　Row-sparsity　Binary　Feature　Learning-based　Multiview　(RsBFL-Mv)　representation,　for　palmprint　recognition.　Specifically,　given　the　training　multiview　data,　RsBFL-Mv　jointly　learns　multiple　projection　matrices　that　transform　the　informative　multiview　features　into　discriminative　binary　codes.　Afterwards,　the　learned　binary　codes　of　each　view　are　converted　to　the　real-value　map.　Following　this,　we　calculate　the　histograms　of　multiview　feature　maps　and　concatenate　them　for　matching.　For　RsBFL-Mv,　we　enforce　three　criteria:　1)　the　quantization　error　between　the　projected　real-valued　features　and　the　binary　features　of　each　view　is　minimized,　at　the　same　time,　the　projection　error　is　minimized;　2)　the　salient　label　information　for　each　view　is　utilized　to　minimize　the　distance　of　the　within-class　samples　and　simultaneously　maximize　the　distance　of　the　between-class　samples;　3)　the　l2,1　norm　is　used　to　make　the　learned　projection　matrices　to　extract　more　representative　features.　Extensive　experimental　results　on　two　publicly　accessible　palmprint　datasets　demonstrated　the　effectiveness　of　the　proposed　method　in　recognition　accuracy　and　computational　efficiency.　Furthermore,　additional　experiments　are　conducted　on　two　commonly　used　finger　vein　datasets　that　verified　the　powerful　generalization　capability　of　the　proposed　method.　2637-6407　©　2024　IEEE.