Finger　vein　biometric　technology　has　gained　a　lot　of　popularity　over　recent　years.　This　is　primarily　due　to　the　increased　security　and　reliability　level　that　comes　with　its　non-intrusive　nature.　Non-intrusiveness　became　inevitable　due　to　the　pandemic　of　COVID-19.　This　paper　introduces　a　unique　and　lightweight　image　enhancement　method　for　person　identification　using　Convolutional　Neural　Networks　(CNN).　As　pre-processing　steps,　Contrast　Limited　Adaptive　Histogram　Equalization　(CLAHE)　followed　by　gamma　correction　is　applied.　Afterward,　the　image　is　sharpened　and　then　passed　through　the　median　filter.　These　steps　are　followed　by　applying　power　law　and　contrast　adjustment.　As　a　final　step,　CLAHE　is　used　yet　again　to　bring　out　the　enhanced　vascular　structure.　The　method　was　appraised　using　the　four　different　openly　accessible　databases.　These　are　regarded　as　the　most　challenging　available　finger　vein　database-s　by　many　researchers.　For　recognition　purposes,　CNN　was　used　with　transfer　learning.　Transfer　learning　is　implemented　by　modifying　the　13　convolutional　layers　of　VGG-16.　The　proposed　model　architecture　also　includes　five　max-pooling　layers,　one　ReLU,　and　one　Softmax　layer.　It　is　observed　that　with　transfer　learning,　the　accuracy　could　have　reached　up　to　99%　on　finger-vein　recognition　on　the　experimented　dataset,　thus　proved　to　be　a　highly　accurate　approach　for　finger　vein　recognition.