Optimal core point detection using multi-scale principal component analysis

Abstract

Core point plays a vital role in fingerprint matching and classification. The fingerprint images may be of poor quality because of sensor type and user’s body condition. To detect the core point in noisy and poor quality fingerprint images, we have estimated the dominant orientation field based on principal component analysis and multi-scale pyramid decomposition to produce correct orientation field. The proposed work detects the optimal upper and lower core points using shape analysis of orientation field and binary candidate region images in fingerprints. Experiments are carried out on FVC databases and it is found that the proposed algorithm has high accuracy in locating exact core points.