Multi-scaling Detection of Singular Points Based on Fully Convolutional Networks in Fingerprint Images

Jin, Qin; Han, Congying; Bai, Chaochao; Guo, Tong

doi:10.1007/978-3-319-69923-3_24

Cited by 7 publications

(7 citation statements)

References 9 publications

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“…In this subsection, we compare the detection performance on FVC2002 DB1 and NIST sd04 public datasets to demonstrate the effect of the proposed deep learning detection method with conventional detection algorithms [6,7,27] and deep learning detection method [17]. The detection rate and false alarm rate are chosen as the indicators measured.…”

Section: Singular Points Detection Performancementioning

confidence: 99%

“…In addition, the detection speed of our method is about 95 milliseconds on a GPU averagely. Figure 5 shows some illustrations over the two methods in [7,17] and our method. As shown in Figure 5, our method and the method of Qin et al [17] could achieve better effect than the method of Fan et al [7] on poor quality images for location information of singular points.…”

Section: Singular Points Detection Performancementioning

confidence: 99%

“…Figure 5 shows some illustrations over the two methods in [7,17] and our method. As shown in Figure 5, our method and the method of Qin et al [17] could achieve better effect than the method of Fan et al [7] on poor quality images for location information of singular points. For some special issues (e.g., arch fingerprint images), our method could perform better than the two methods.…”

Section: Singular Points Detection Performancementioning

confidence: 99%

“…Figure 5. Illustrations of singular points detection over our method, the method of Fan et al [7], the method of Qin et al [17] from left to right. Red dots and blue triangles denote the core points and delta points respectively, while the red arrows represent the orientations of up-core points.…”

Section: Singular Points Detection Performancementioning

confidence: 99%

“…Labati et al [16] proposed a two-step CNN method to extract the coordinates of the sweat pores from fingerprint images, which perform first the detection of candidate regions and Then, the extraction of the points of interest in the second step. Besides, Qin et al [17] used fully convolutional networks (FCN) [18] to detect fingerprint singular points. Nevertheless, this method only predict location information and obtain poor performance on some specific fingerprint images, such as the issue that two core points are very close.…”

Section: Introductionmentioning

confidence: 99%

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A Method for Singular Points Detection Based on Faster-RCNN

et al. 2018

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Most methods for singular points detection usually depend on the orientation fields of fingerprints, which cannot achieve reliable and accurate detection of poor quality fingerprints. In this study, a new method for fingerprint singular points detection based on Faster-RCNN (Faster Region-based Convolutional Network method) is proposed, which is a two-step process, and an orientation constraint is added in Faster-RCNN to obtain orientation information of singular points. Besides, we designed a convolutional neural network (ConvNet) for singular points detection according to the characteristics of fingerprint images and the existing works. Specifically, the proposed method could extract singular points directly from raw fingerprint images without traditional preprocessing. Experimental results demonstrate the effectiveness of the proposed method. In comparison with other detection algorithms, our method achieves 96.03% detection rate for core points and 98.33% detection rate for delta points on FVC2002 DB1 dataset while 90.75% for core points and 94.87% on NIST SD4 dataset, which outperform other algorithms.

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Section: Singular Points Detection Performancementioning

confidence: 99%

Section: Singular Points Detection Performancementioning

confidence: 99%