Abstract
Partially defective fingerprint image (PDFI) with poor performance poses challenges to the automated fingerprint identification system (AFIS). To improve the quality and the performance rate of PDFI, it is essential to use accurate segmentation. Currently, most fingerprint image segmentations use methods with ridge orientation, ridge frequency, coherence, variance, local gradient, etc. This paper proposes a method of XFinger-Net for segmenting PDFIs. Based on U-Net, XFinger-Net inherits its characteristics. The attention gate with fewer parameters is used to replace the cascaded network, which can suppress uncorrelated regions of PDFIs. Moreover, the XFinger-Net implements a pixel-level segmentation and takes non-blocking fingerprint images as an input to preserve the global characteristics of PDFIs. The XFinger-Net can achieve a very good segmentation effect as demonstrated in the self-made fingerprint segmentation test.
Highlights
The purpose of fingerprint image segmentation is to separate the fingerprint foreground from the fingerprint image, which is one of the determinants of performance in the automatic fingerprint recognition system
The attention gate with fewer parameters used in the XFinger-Net replaces the cascaded network, aiming to suppress uncorrelated regions in the input image while highlighting the region of interest (RoI) of the partially defective fingerprint images (PDFIs), forming an important part of this study
Huckemann, 2016 [7] proposed a new approach for fingerprint segmentation from three aspects. They used factorized directional bandpass (FDB) and directional Hilbert transform originated from Butterworth bandpass (DHBB) filter combined with soft-thresholding for texture extraction
Summary
The purpose of fingerprint image segmentation is to separate the fingerprint foreground from the fingerprint image, which is one of the determinants of performance in the automatic fingerprint recognition system. The attention gate with fewer parameters used in the XFinger-Net replaces the cascaded network, aiming to suppress uncorrelated regions in the input image while highlighting the region of interest (RoI) of the partially defective fingerprint images (PDFIs), forming an important part of this study. Huckemann, 2016 [7] proposed a new approach for fingerprint segmentation from three aspects They used factorized directional bandpass (FDB) and directional Hilbert transform originated from Butterworth bandpass (DHBB) filter combined with soft-thresholding for texture extraction. Jain proposed a fingerprint image segmentation method and a deep network model SegFinNet [8] in the field of latent fingerprints. The gate model implicitly learns to suppress irrelevant regions in the input image input image while highlighting salient features that are useful for a particular task.
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