基于指纹方向场全局特征的伪核减少

X. Ye, Hepeng Wang, Rumeng Zou, Yirui Liu, Huahua Chen, Yuzhong Shen
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引用次数: 0

摘要

奇异点检测是指纹识别的首要步骤。已经提出了许多方法,但正误差率仍然太高。提出了一种基于指纹方向场全局信息的减少假核的方法。该方法首先估计像素级方向场,然后利用经典的基于嵌套庞加莱指数的方法提取候选奇异点。然后,计算每个候选核的打开方向以及与打开方向相反方向的相应区域。最后,计算岩心打开方向与打开方向相反方向区域的方向场夹角作为新特征,进一步减少候选集中的假岩心。实验结果表明,该方法的错误率低于传统算法。与基于npi的FVC2000-DB2a数据库[12]相比,总错误率降低了29.5%,核心误报率降低了35.1%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
False Cores Reduction Based on Global Features of Fingerprint Orientation Field
Singular point detection is a primary step in fingerprint recognition. Many approaches have been proposed, but the positive error rate is still too high. This paper proposes a new method based on global information of fingerprint orientation field to reduce false cores. The proposed method first estimates pixel-level orientation field and extract candidate singular points using the classic nested-Poincare index-based method. Then, both the opening direction of each candidate cores and the corresponding region along the opposite direction to the opening direction are calculated. Finally, the angle between the opening direction of a core and the orientation field in the region along the opposite direction of the opening direction is calculated as a new feature to further reduce false cores in the candidate set. Experimental results show that the error rate of the proposed method is lower than traditional algorithms. The total error rate decreases 29.5% and the false positive rate of cores decreases 35.1% comparing with NPI-based [12] in the database of FVC2000-DB2a.
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