The method based on Multiple Instance Learning (MIL) can alleviate the drift problem to a certain extend. However, MIL method has relatively poor performance in running efficiency and accuracy, because the update strategy efficiency of the strong classifiers is low, and the update speed of the classifiers is not same with the appearance change speed of the targets. To solve this problem, a new update strategy for strong classifier was proposed to improve the running efficiency of MIL method. In addition, to improve the tracking accuracy of the MIL method, a new dynamic mechanisim for learning rate renewal of the classifier was given to make the updated classifier would more conform to the appearance of the target. The experimental results on comparison with MIL method and the Weighted Multiple Instance Learning (WMIL) method show that, the proposed method has the best performance in running efficiency and accuracy among the three methods, and has an advantage over tracking when there is no similar interference objects to target objects in background.

In order to overcome the impacts of image rotation on the accuracy of the finger-vein identification system, in the image pre-processing module, a new rotation approach based on the fingertip position to correct the target region was presented in this paper. The other part of the proposed system consists of an improved feature extraction module using direction templates and local dynamic threshold segmentation and a classification module using a Modified Hausdorff Distance (MHD). The experimental results show that it achieves a high recognition rate of 97.25%, and the Equal Error Rate (EER) is 0.75% when the drift angle among the images of the same finger is less than 20 degrees. What is more, the whole process took only 161.6949ms in VC〖KG-*3〗+〖KG-*3〗+6.0, which meant that the system had a superior real-time performance. The proposed system also has certain practical significance for developing finger-vein identification products.