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An algorithm is proposed, which combines global and local information of fingerprint images to detect singular points. It’s mathematically proven that normal lines of gradient of double orientation field(GDOF) pass through singular points. Normal lines of GDOF use rather global information to detect candidate singular points. Fingerprint image is d...
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... points can be used in fingerprint pattern classification [1][2][3][4] to reduce search space in large databases and as reference points in fingerprint minutiae matching [5,6]. Singular points are core and delta, as illustrated in Figure 1. Local information is sensitive to noise. ...
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... to Henry classification rules [25], arch has no singular point, tent arch or loop has one pair of singular points, whorl or twin has two pairs of singular points. In theory, normal lines should detect two pairs of singular points for whorl or twin, as Figures 10(b) and (d) show. Sometimes, the second large gray-value block is a result of noise. ...
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... a lot of whorls or twins, their two cores are fairly close. The two cores detected by normal lines are so close that they cannot be distinguished from each other, as Figure 11(b) shows. For the above reasons, only the block that has the maximum gray value is considered to be a candidate singular point. ...
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... the detected core and delta are both erased. Figure 12 shows the detected singular points on arch. In Figures 12(b)-(e), a lighter block means a larger gray value. ...
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... 12 shows the detected singular points on arch. In Figures 12(b)-(e), a lighter block means a larger gray value. As Figure 12 is arch, the detected singular points are erased. ...
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... Figures 12(b)-(e), a lighter block means a larger gray value. As Figure 12 is arch, the detected singular points are erased. So Figure 12(f) is same with Figure 12(a). ...
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... Figure 12 is arch, the detected singular points are erased. So Figure 12(f) is same with Figure 12(a). If a fingerprint image is not arch, it's necessary to confirm whether it is whorl or twin or not. ...
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... Figure 12 is arch, the detected singular points are erased. So Figure 12(f) is same with Figure 12(a). If a fingerprint image is not arch, it's necessary to confirm whether it is whorl or twin or not. ...
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... the second core is verified, a fingerprint is classified into whorl or twin. If ridge line is traced long enough, and no large curvature region is found, the fingerprint image is classified into tent arch or loop, as Figure 13 shows. In a fingerprint, core and delta exist in pair(s) [25]. ...
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... Table 4, it can be seen that our algorithm works better than ZP method in core detection, but worse in delta detection. Figure 14 shows a comparison of results in Figure 9 using our algorithm, ZP method and Poincare index, respectively. Singular points can be used as reference points in fingerprint minutiae matching. ...
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Citations
... Among existing methods of SP detection, Poincare Index (PI), introduced by Kawagoe and Tojo [6], is a classical one, and has been widely used because of the advantages of its simple design, the robustness against image rotation, and the ability to distinguish SP types. For instance, Fan et al. [7] and Jin and Kim [8] proposed improved methods that built upon PI, respectively. However, PI-based methods are sensitive to noise. ...
... To deal with this problem, some researchers attempted to combine PI with other methods [9]. Meanwhile, non-PI-based methods were proposed such as orientation curvature based approach [7,10]. ...
... For example, there still exist big discrepancies between automatically detected SPs and those manually identified by Henry's definition. Mostly existing methods of SP detection are based on block-level orientation field [4,5,7,9,10], and consequently the center of a block is usually regarded as a SP's location. Bazen and Gerez [11] combined Principal Components Analysis(PCA) and PI to compute high resolution Orientation Field (OF) and further detect SPs at pixel-level, but there are often more false-positive SPs in detection results because of linear filters used for computing high resolution OF. ...
Singular point detection is a primary step in fingerprint recognition, especially for fingerprint alignment and classification. But in present there are still some problems and challenges such as more false-positive singular points or inaccurate reference point localization. This paper proposes an accurate core point localization method based on spatial domain features of fingerprint images from a completely different viewpoint to improve the fingerprint core point displacement problem of singular point detection. The method first defines new fingerprint features, called furcation and confluence, to represent specific ridge/valley distribution in a core point area, and uses them to extract the innermost Curve of ridges. The summit of this Curve is regarded as the localization result. Furthermore, an approach for removing false Furcation and Confluence based on their correlations is developed to enhance the method robustness. Experimental results show that the proposed method achieves satisfactory core localization accuracy in a large number of samples.
This paper focuses on a general framework for singular point extraction from vector field. We design a new index of singular point based on complex polynomial model. We test our method in the publicly available benchmark dataset of the singular point detection competition (SPD2010). Our algorithm gets the best results and produces large margins compared to the top five algorithms which took part in the public competition. We also compare our algorithm with the state-of-the-art singular point detection algorithm (called 'ZPM' method) with the benchmark. The performance of our method is much better than that of the state-of-the-art method.
