Abstract: Recently, Melnik proposed a new rank level classifier fusion idea, which managed to keep a balance between the preference for the specific classifier and the confidence it had in any specific rank. However, Melnik's classifier fusion method suffers from ``the curse of dimensionality''. The number of parameters increases exponentially with the increase of the number of classifiers. Inspired by Melnik's idea, we propose a new fusion method, which achieves Melnik's objectives through combination of the rank transforming and the weighted classifier integration. Furthermore, a continuously differentiable classification error expression is given. Based on that, a gradient descendent parameter tuning algorithm is designed. We develop a multi-modal identity recognition system by fusion of palmprint and finger image data. Many experiments have been conducted to test the performance of our method under the condition of different classifier numbers. The experimental results show that the performance of our method is better than those of traditional methods and Melnik's method.