To study the fusion at feature extraction level for fingerprint and finger vein biometrics, a dynamic weighting matching algorithm based on predictive quality evaluation of interest features is proposed. The proposed approach is based on the fusion of the two traits by extracting independent feature point-sets from the two modalities, and making the two point-sets compatible for concatenation. According to the results of features evaluation, dynamic weighting strategy is introduction for the fusion biometrics. The weight of excellent features in fusion is improved, aiming to weaken the influence of low quality and false features so that better effects of fusion can be achieved. Experimental results based on FVC2000 and self-constructed databases of finger vein show that our scheme achieves 98.9% recognition rate, compared with fingerprint recognition and finger vein recognition increased by 6.6% and 9.6% respectively, compared with fusion recognition at matching level increased by 5.4%.