ESTIMATION OF FINGERPRINT ORIENTATION FIELD BY UTILIZING PRIOR KNOWLEDGE AND SELF-ORGANIZING MAP

Abstract

This study proposes a new method of estimating fingerprint orientation field by utilizing prior knowledge of fingerprint images and Self-Organizing Map (SOM). The method is based on the assumption that fingerprint images have some common properties that can be systematized to build prior knowledge. In this method, each fingerprint image was divided into 16 regions equally and the regions were analyzed separately. To analyze the regions, they were divided into blocks of 8x8 pixels. Feature vector of each block was constructed by summing the pixel intensity values in row and column wise. Furthermore, feature vectors of the blocks were concatenated to form a feature matrix of the region. The matrix was then processed by SOM to find the most dominant orientation field of the region. The experiment results showed that the chosen feature gave short epochs of SOM training. In addition, the method was able to estimate the orientation field of most regions. However, the method could not precisely determine the orientation field if the blocks are dominated by background pixels.