首页> 外文OA文献 >ADAPTABLE FINGERPRINT MINUTIAE EXTRACTIONudALGORITHM BASED-ON CROSSING NUMBERudMETHOD FOR HARDWARE IMPLEMENTATIONudUSING FPGA DEVICE
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ADAPTABLE FINGERPRINT MINUTIAE EXTRACTIONudALGORITHM BASED-ON CROSSING NUMBERudMETHOD FOR HARDWARE IMPLEMENTATIONudUSING FPGA DEVICE

机译:自适应指纹分钟记录提取 ud基于交叉编号的算法 ud硬件实施方法 ud使用FPGA设备

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摘要

In this article. a main perspective of developing and implementing fingerprint extraction and matchingudalgorithms as a pari of fingerprint recognition system is focused. First, developing a simple algorithm toudextract fingerprint features and test this algorithm on Pc. The second thing is implementing this algorithmudinto FPGA devices. The major research topics on which the proposed approach is developing andudmodifying fingerprint extraction feature algorithm. This development and modification are using crossingudnumber method on pixel representation value '0'. In this new proposed algorithm, it is no need a processudconcerning ROI segmentation and no trigonometry calculation. And specially in obtaining their parametersudusing Angle Calculation Block avoiding floating points calculation. As this method is local feature thatudusually involve with 60-100 minutiae points, makes the template is small in size. Providing FAR. FRR andudEER, performs the performance evaluation of proposed algorithm. The result is an adaptable fingerprintudminutiae extraction algorithm into hardware implementation with 14.05 % of EEl?, better than referenceudalgorithm, which is 20.39 % . The computational time is 18 seconds less than a similar method, which takesud60-90 seconds just for pre-processing step. The first step of algorithm implementation in hardwareudenvironment (embedded) using FPGA Device by developing IP Core without using any soft processor isudpresented.
机译:在这篇文章中。重点介绍了开发和实现作为指纹识别系统的一部分的指纹提取和匹配算法的主要观点。首先,开发一种简单的算法提取指纹特征并在PC上测试该算法。第二件事是在FPGA器件中实现该算法。提出的方法正在开发和修改指纹提取特征算法的主要研究主题。此开发和修改对像素表示值“ 0”使用cross udnumber方法。在这种新提出的算法中,不需要关于ROI分割的过程,也不需要三角计算。特别是在获取其参数时使用角度计算模块避免了浮点计算。由于此方法是 60细节点通常涉及的局部特征,因此模板较小。提供FAR。 FRR和 udEER,对提出的算法进行性能评估。结果是在硬件实现中采用了一种适应性强的指纹指纹提取算法,具有14.05%的EEl ?,优于参考 udal算法,后者为20.39%。计算时间比类似的方法少18秒,后者仅需 ud60-90秒即可完成预处理步骤。展示了在不使用任何软处理器的情况下通过开发IP内核在使用FPGA器件的硬件环境(嵌入式)中实现算法的第一步。

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