Vein authentication is a novel biometric method to authenticate the individuality of a person. The conventional biometric technique employs shape images and exact segments of finger veins for the verification process. To improve the verification accuracy, a novel Anisotropic Filtered Stromberg Feature Transform based on Tucker’s Congruence Deep Belief Structure Learning (AFSFT-TCDBSL) technique is intended. The proposed AFSFT-TCDBSL technique comprises one input, three hidden, and one output layers. The numbers of images are collected in the input, and input images are preprocessed using anisotropic diffusion filtering in the first hidden layer. Finally, the verification process is performed using Tucker’s congruence correlation coefficient (TCCC). Based on the correlation, the verification outputs are getting to the output layer. In this way, accurate finger vein verification is performed with superior accuracy and with a minimum false rate. We performed experimental assessments with different factors, such as PSNR, FVVA, FPR, and CT. The proposed ADFSFT-TCDBSL technique offers better finger-vein verification results than the state-of-the-art methods.