Due　to　their　high　reliability,　security,　and　anti-counterfeiting,　finger-based　biometrics　(such　as　finger　vein　and　finger　knuckle　print)　have　recently　received　considerable　attention.　Despite　recent　advances　in　finger-based　biometrics,　most　of　these　approaches　leverage　much　prior　information　and　are　non-robust　for　different　modalities　or　different　scenarios.　To　address　this　problem,　we　propose　a　structured　Robust　and　Sparse　Least　Square　Regression　(RSLSR)　framework　to　adaptively　learn　discriminative　features　for　personal　identification.　To　achieve　the　powerful　representation　capacity　of　the　input　data,　RSLSR　synchronously　integrates　robust　projection　learning,　noise　decomposition,　and　discriminant　sparse　representation　into　a　unified　learning　framework.　Specifically,　RSLSR　jointly　learns　the　most　discriminative　information　from　the　original　pixels　of　the　finger　images　by　introducing　the　l(2,1)　norm.　A　sparse　transformation　matrix　and　reconstruction　error　are　simultaneously　enforced　to　enhance　its　robustness　to　noise,　thus　making　RSLSR　adaptable　to　multi-scenarios.　Extensive　experiments　on　five　contact-based　and　contactless-based　finger　databases　demonstrate　the　clear　superiority　of　the　proposed　RSLSR　in　terms　of　recognition　accuracy　and　computational　efficiency.