Robust Fingerprint Singular Point Detection using a Single-Stage CNN for Object Detection

28th International Conference on Systems, Signals and Image Processing (IWSSIP)

Lucas de Sousa Fernandes1   João Pedro Bernardino Andrade1   Leonardo Ferreira da Costa1
Paulo Bruno de Sousa Serafim1   Paulo Antonio Leal Rego1,3   José Gilvan Rodrigues Maia1,2

1Federal University of Ceará (UFC)  2Virtual UFC Institute
3Group of Computer Networks, Software Engineering and Systems (GREat)

IWSSIP 2021


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Abstract

An Automated Fingerprint Identification System (AFIS) is the cornerstone of many modern identity-driven applications, ranging from device authentication and law enforcement to security and borderline control. As the population in urban centers grows and the digitalization of services grows, so does the demand for more effective and efficient fingerprint recognition systems. Singular Points (SP), such as core and delta, are important landmarks that help to tackle this challenge. This paper proposes and evaluates an effective approach for SP detection based on a single-stage deep convolutional neural network model for object detection. We show that YOLOv4 detector with customized output layers is effective for handling cores and deltas patterns as patches in fingerprint images, using their center as coordinates. Experimental results were carried out on the challenging SPD2010 dataset to evaluate the proposed SP detector under different configurations. The best result is 60.34% of correctly detected fingerprints. In particular, compared to the state-of-the-art methods, our approach achieves an improvement up to 12% in correct detections, 8% in core detection rate, and 10% in delta detection rate. Core and delta miss rates are also reduced by 8% and 10%, respectively.

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@InProceedings{fernandes2021robust,
  title = {Robust Fingerprint Singular Point Detection using a Single-Stage CNN for Object Detection},
  author  = {Fernandes, Lucas de Sousa  and
    Andrade, Jo{\~{a}}o Pedro Bernardino and
    Costa, Leonardo Ferreira and
    Serafim, Paulo Bruno Sousa and
    Rego, Paulo Antonio Leal and
    Maia, Jos\'{e} Gilvan Rodrigues},
  booktitle = {28th International Conference on Systems, Signals and Image Processing (IWSSIP)},
  pages = {1--12},
  year = {2021}
}