Fingerprint matching application using hardware based artificial neural network with Matlab

S. Sudiro, I. P. Wardhani, B. A. Wardijono, Brian Handias
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引用次数: 1

Abstract

Biometric-based system is one of the systems for identification/recognition and verification. There are many types of biometric technology, one of them is fingerprint recognition system. Fingerprint, like other biometric feature is unique, means that it will always different with its kind. One of the problems in fingerprint recognition systems is how to extract the characteristics/feature of a person's fingerprint. This article present fingerprint matching application as part of recognition, using simple minutiae point extraction algorithm implemented it into hardware-based Artificial Neural Network device. The Cogniblox is an ANN device from CogniMem that comes with 4 CM1K chip, able to store up to 4096 neurons in the device. MATLAB environment is used for developing the fingerprint recognition application. The designed system is then undergo a performance evaluation by recognizing a total of 80 dataset of fingerprint, consist of 10 different fingerprint with 8 impression each, which stored into the neurons beforehand. The performance evaluation resulted in a False Acceptance Rate (FAR), False Rejection Rate (FRR) and Equal Error Rate (EER) are presented.
指纹匹配应用基于硬件的人工神经网络,用Matlab编写
基于生物特征的系统是身份识别和验证的系统之一。生物识别技术有很多种,指纹识别系统就是其中之一。指纹和其他生物特征一样是独一无二的,这意味着它总是与同类不同。指纹识别系统的问题之一是如何提取人的指纹特征。本文将指纹匹配应用作为识别的一部分,利用简单的细节点提取算法将其实现到基于硬件的人工神经网络设备中。Cogniblox是CogniMem公司的一款神经网络设备,配备4个CM1K芯片,可在设备中存储多达4096个神经元。采用MATLAB环境开发指纹识别应用程序。设计的系统通过识别80个指纹数据集进行性能评估,这些指纹数据集由10个不同的指纹组成,每个指纹有8个印象,并预先存储在神经元中。给出了错误接受率(FAR)、错误拒绝率(FRR)和等错误率(EER)的性能评价结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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