Core point detection using improved segmentation and orientation

Core point detection is very important in fingerprint classification and matching process. Usually fingerprint images have noisy background and the local orientation field also changes very rapidly in the singular point area. It is difficult to locate the singular point precisely. In this paper, we present a new algorithm for optimal core point detection using improved segmentation and orientation. In our technique detects core point accurately by extracting best region of interest(ROI) from image and using fine orientation field estimation. We present a modified technique for extracting ROI and fine orientation field. The distinct feature of our technique is that it gives high detection percentage of core point even in case of low quality fingerprint images. The proposed algorithm is applied on FVC2004 database. Results of experiments demonstrate improved performance for detecting core point.