A Novel Approach of Personal Identification Based on Single Knuckleprint Image

A novel approach which use single knuckleprint image only to implement personal identification is presented in this paper. Unlike most previous work, there is no need to collect a large amount of images to train the classifier. The identification process can be divided into the following stages: extracting the feature of the knuckleprint and matching the line feature. For the texture characteristic of the knuckleprint, we use a Self-defined convolution template as the gradient operator to carry out the edge detection and extract it’s line feature. Moreover, In order to solve the dislocation of the images between the matching images, a new method for line feature matching is proposed, which improved the correct identification rate at a large extent. The approach was tested on a database of 98 people (1,579 Knuckleprint images). The experimental results showed the effectiveness of the method is obviously.