Over　the　last　decades,　finger　vein　biometric　recognition　has　generated　increasing　attention　because　of　its　high　security,　accuracy,　and　natural　anti-counterfeiting.　However,　most　of　the　existing　finger　vein　recognition　approaches　rely　on　image　enhancement　or　require　much　prior　knowledge,　which　limits　their　generalization　ability　to　different　databases　and　different　scenarios.　Additionally,　these　methods　rarely　take　into　account　the　interference　of　noise　elements　in　feature　representation,　which　is　detrimental　to　the　final　recognition　results.　To　tackle　these　problems,　we　propose　a　novel　jointly　embedding　model,　called　Joint　Discriminative　Analysis　with　Low-Rank　Projection　(JDA-LRP),　to　simultaneously　extract　noise　component　and　salient　information　from　the　raw　image　pixels.　Specifically,　JDA-LRP　decomposes　the　input　image　into　noise　and　clean　components　via　low-rank　representation　and　transforms　the　clean　data　into　a　subspace　to　adaptively　learn　salient　features.　To　further　extract　the　most　representative　features,　the　proposed　JDA-LRP　enforces　the　discriminative　class-induced　constraint　of　the　training　samples　as　well　as　the　sparse　constraint　of　the　embedding　matrix　to　aggregate　the　embedded　data　of　each　class　in　their　respective　subspace.　In　this　way,　the　discriminant　ability　of　the　jointly　embedding　model　is　greatly　improved,　such　that　JDA-LRP　can　be　adapted　to　multiple　scenarios.　Comprehensive　experiments　conducted　on　three　commonly　used　finger　vein　databases　and　four　palm-based　biometric　databases　illustrate　the　superiority　of　our　proposed　model　in　recognition　accuracy,　computational　efficiency,　and　domain　adaptation.