Multi Layered Minutiae Extraction Based on Fusion Attention for OCT Fingerprints

Minutiae extraction plays an important role in automated fingerprint identification systems. There are many minutiae extraction methods proposed for various fingerprints (rolled/slap/latent) in the past decades. However, for fingerprints by optical coherence tomography (OCT) imaging which have the essential property of multi-layered tissue structure, previous work may not be suitable. This paper is devoted to extract minutiae for OCT-based fingerprints based on the characteristics of OCT imaging and powerful deep neural networks. Specifically, we propose a multi-tissue-layered minutiae extraction network based on fusion-attention, involving a multi-tissue-layered feature extractor to fully explore the rich information in multiple subsurface fingerprints, and a one-stage detection framework to detect minutiae points. To demonstrate the effectiveness and robustness of the proposed method, a series of experiments have been carried out on the public OCT fingerprint benchmark dataset ( https://github.com/FengLiu-0013/ ). Our method obtains the best results with an improvement of 8.3% in F1-score compared with state-of-the-art methods. The proposed method has also been evaluated for fingerprint identification using the extracted minutiae. Our method has achieved a matching error rate of 1.57%, with a relative reduction of 41% compared with state-of-the-art methods.