Finger vein biometric technology has gained a lot of popularity over recent years. This is primarily due to the increased level of security and reliability that comes with it and also because it is non-intrusive in nature. This paper introduces a unique and lightweight image enhancement method for the purpose of finger vein identification using Convolutional Neural Networks (CNN). After finger detection, a methodology of Contrast Limited Adaptive Histogram Equalization (CLAHE) is performed on the image, which is followed by gamma correction. The image is then sharpened and median filtered. After that, the power-law is applied and then contrast adjustment is made. As a final step, CLAHE is used yet again to bring out the enhanced vascular structure. The method was appraising the four openly accessible different databases, which were examined as the most challenging available finger vein databases by many researchers. For recognition purposes, CNN was used with transfer learning. For the implementation of transfer learning, the architecture of VGG- 16 was modified to include 13 convolutional layers, five max-pooling layers, and 1 ReLU and one softmax layer. It was observed that with transfer learning, accuracy reached up to 99%, thus making it a highly accurate approach for finger vein recognition.