Quality-Aware Fusion of Multisource Internal and External Fingerprints Under Multisensor Acquisition

Abstract

Fingerprint is one of the most widely used biometric features. The researches on external fingerprint collected by total internal reflection and the internal fingerprint obtained by optical coherence tomography have been carried out extensively. Studies proved the consistency between them. The external fingerprint is susceptible to the fingertip status, whereas the internal fingerprint has strong anti-interference and anti-spoofing capabilities. They originate from different skin layers and are collected by multiple sensors. Given the corresponding advantages and disadvantages of external and internal fingerprints, their fusion can maximize effective information and improve fingerprint image quality; thus, this fusion is conducive to fingerprint identification. In this study, a fingerprint fusion method based on the quality-aware convolutional-sparsity-based morphological component analysis (CSMCA) is proposed. The proposed method realizes the fusion of multisource and multisensor fingerprints for the first time. Quality indexes, namely, spatial consistency, dryness, and humidity, are selected. Moreover, a simulation-based combination scheme is proposed for the pixel-level quality representation. The quality index is integrated with CSMCA for quality-aware fusion, which retains high-quality components and reduces low-quality areas. The experiments prove the superiority of the proposed method in quality scores and matching performances, indicating that internal fingerprints can amend external fingerprints. The matching experiments with either external or internal fingerprints show that the fused fingerprints can be compatible with the existing fingerprint databases. Our work can provide references and insights into identifying mutilated fingerprints in the future.