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Singular Points Detection in Fingerprints Based on Poincare Index and Local Binary Patterns

机译:基于Poincare指数和局部二值模式的指纹奇异点检测

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摘要

In this study, a singular points detection method based on low-resolution image processing technique and Poincare index algorithm was introduced. First, 2D discrete wavelet transform (DWT) was used to conduct fingerprint image operation, then a LL-band image that has 1/4 resolution of the original image was obtained to reduce the operation needed in the subsequent processing. After finishing the detection procedure of singular point, each possible singular point was taken as the center to calculate the local binary pattern (LBP) features at its peripheral as reference standard for selecting correct singular point. Through this way, fake singular point is removed. The experimental result shows that the method in this study could indeed reduce the chance to detect fake singular points due to noise effect. Meanwhile, the processing speed of the traditional Poincare index method can be speeded up. (C) 2019 Society for Imaging Science and Technology.
机译:提出了一种基于低分辨率图像处理技术和庞加莱指数算法的奇异点检测方法。首先,使用2D离散小波变换(DWT)进行指纹图像操作,然后获得具有原始图像分辨率1/4的LL波段图像,以减少后续处理所需的操作。在完成奇异点的检测过程之后,以每个可能的奇异点为中心,计算其外围的局部二值模式(LBP)特征作为选择正确奇异点的参考标准。通过这种方式,伪造的奇异点被去除了。实验结果表明,该研究方法确实可以减少由于噪声影响而检测到假奇异点的机会。同时,可以加快传统Poincare指数方法的处理速度。 (C)2019影像科学与技术学会。

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