SINGULAR POINT DETECTION FOR EFFICIENT FINGERPRINT CLASSIFICATION
SINGULAR POINT DETECTION FOR EFFICIENT FINGERPRINT CLASSIFICATION
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发表时间：
2012
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通讯作者：
A. Awad;K. Baba
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作者：
A. Awad;K. Baba
指纹上的奇异点或奇异点由于其尺度、平移和旋转不变性而被认为是指纹界标。它用于自动指纹识别系统中的指纹分类和对齐。本文提出了一种比较研究的两个奇异点检测方法在文献中。对Poincare指数法和复滤波法进行了对比研究，旨在从处理时间和检测精度两方面寻找最优的奇异点检测方法。此外，发现这两种方法的处理时间瓶颈是提高其性能的一个高级步骤。在处理时间和检测精度方面的最佳检测方法将被更新以适合我们的高效分类方法。这两种方法进行的实验评估证明，通过复杂的过滤器实现的最大精度高达95%，具有相当的处理时间和90%的庞加莱指数的方法具有更高的处理时间。
A singular point or singularity on fingerprint is considered as a fingerprint landmark due its scale, shift, and rotation immutability. It is used for both fingerprint classification and alignment in automatic fingerprint identification systems. This paper presents a comparative study between two singular point detection methods available in the literature. The comparative study has been conducted on the Poincare index and the complex filter methods, and it aims to catch the optimum singular point detection method in terms of the processing time and the detection accuracy. Moreover, discovering the processing time bottlenecks for both methods is an advanced step to improve the their performance. The optimum detection method in both processing time and detection accuracy will be updated to suite our efficient classification method. The conducted experimental evaluation for both methods proved that the maximum accuracy achieved by the complex filter is up to 95% with a considerable processing time and 90% with the Poincare index method with a higher processing time.