A multi-task fully deep convolutional neural network for contactless fingerprint minutiae extraction

作者：

Highlights：

• A multi-task fully deep convolutional neural network is proposed for contactless fingerprint minutiae extraction.

• A new loss function is proposed to jointly learn the tasks of minutiae detection and its direction regression from the whole fingerprints.

• The proposed method operates directly on the gray scale contactless fingerprints without any preprocessing.

• Experiments on three datasets have shown the effectiveness of the proposed algorithm.

摘要

•A multi-task fully deep convolutional neural network is proposed for contactless fingerprint minutiae extraction.•A new loss function is proposed to jointly learn the tasks of minutiae detection and its direction regression from the whole fingerprints.•The proposed method operates directly on the gray scale contactless fingerprints without any preprocessing.•Experiments on three datasets have shown the effectiveness of the proposed algorithm.

论文关键词：Contactless fingerprint,Minutiae extraction,Deep convolutional neural network,Multi-task learning

论文评审过程：Received 29 November 2020, Revised 13 June 2021, Accepted 4 July 2021, Available online 21 July 2021, Version of Record 28 July 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.108189