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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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Singular point is an essential global feature in fingerprint images. Existing methods for singular points detection generally visit each pixel or each small image block to determine the singular point. That is to say, existing methods require scanning the image to compute a quantity at each pixel or block, and hence they are inevitably time-consumi...
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This research describes a novel procedure for determining the standard value of the oily distortion of acquisition the fingerprint images based on the score of clarity and ridge-valley thickness ratio. The fingerprint image is quantized into blocks size 32 x 32 pixels. Inside each block, an orientation line, which perpendicular to the ridge directi...
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... 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.
