Incomplete fingerprint recognition based on feature fusion and pattern entropy

Considering the inherent characteristics of incomplete fingerprint: local feature loss and global information distortion, the recognition progress has been mainly restricted by two critical problems: how to precisely extract informative features and still with compact representation of the incomplete fingerprint; and how to effectively measure the similarity between fingerprint images. In this paper, to handle the first problem, both the minutiae and orientation field feature are extracted and then fused to get a more comprehensive feature with scale and rotation invariability. Dealing with the second one, the pattern entropy is introduced to robustly measure the similarity of two incomplete fingerprints. Extensive experiments have been conducted on both those popular fingerprint databases and our extended databases containing more incomplete fingerprints. Meanwhile, thorough performance comparisons have been made with existing approaches. Experimental results show that our approach has more efficient ability especially in incomplete fingerprint recognition, and also performs well in both accuracy and efficiency.