Nonminutiae Based Fingerprint Matching

Jaspreet Kour, Neeta Awasthy
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引用次数: 6

Abstract

Fingerprint verification is one of the most reliable personal identification method. However, manual fingerprint verification is tedious, time consuming and expensive. Thus it is incapable to match the required speed and accuracy expected in modern world. Minutiae based and image based are two major approaches for fingerprint recognition. Image based approach offers much higher computation efficiency with minimum pre processing and proves effective when the image quality is too low to allow a reliable minutiae extraction. In this work, a novel method of image based fingerprint matching which uses the orientation field as a feature vector is proposed. The proposed method overcomes problems of shape distortion, variation in position, rotation and translation of image. Optimal core point detection of fingerprint (image) helps in improving accuracy of fingerprint matching system. Based on orientation feature, a fingerprint matching system is designed. The algorithm has been tested on two databases (10 subjects with five impressions from database available from neurotechnologija and 8 subjects with five impressions from FVC2002 D1_b). The performance of algorithm is measured in terms of receiver operating characteristics. For the neurotechnologija database at 0% false acceptance rate (FAR) the genuine acceptance GAR observed is 75% and at 21.1% FAR, GAR is 95%.For the FVC2002 database at 2.8% FAR the GAR observed is 80% and at 8.66% FAR the GAR is 97.5%.Computational Complexity of proposed algorithm is low and hence can be implemented as automatic fingerprint identification system.
基于非特征的指纹匹配
指纹验证是最可靠的个人身份识别方法之一。然而,手动指纹验证繁琐、耗时且昂贵。因此,它无法与现代世界所要求的速度和准确性相匹配。基于特征和基于图像是指纹识别的两种主要方法。基于图像的方法以最少的预处理提供了更高的计算效率,并且在图像质量过低而无法进行可靠的细节提取时被证明是有效的。本文提出了一种以方向场为特征向量的基于图像的指纹匹配方法。该方法克服了图像的形状畸变、位置变化、旋转和平移等问题。优化指纹(图像)核心点检测有助于提高指纹匹配系统的准确性。基于方向特征,设计了一种指纹匹配系统。该算法已在两个数据库上进行了测试(来自neurotechnologija数据库的10个受试者有5个印象,来自FVC2002 db_b的8个受试者有5个印象)。算法的性能是根据接收机的工作特性来衡量的。对于神经技术数据库,在0%的错误接受率(FAR)下,观察到的真实接受率为75%,在21.1%的错误接受率下,GAR为95%。对于FVC2002数据库,在FAR为2.8%时,GAR为80%,而在FAR为8.66%时,GAR为97.5%。该算法计算复杂度低,可实现指纹自动识别系统。
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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