An Improved Fingerprint Recognition Algorithm Using EBFNN

Luo, Jing; Lin, Shaohui; Ni, Jianyun; Lei, Ming

doi:10.1109/wgec.2008.48

Cited by 14 publications

(3 citation statements)

References 9 publications

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“…An algorithm for Fingerprint recognition using EBFNN. The results show the recognition rate using Ellipsoidal basis function neural-network of 90.5% to 91.8% reported in FVC 2000/ FVC 2002/ FVC 2004 databases which is relatively low as compared to other methods as proposed and discussed in literature [14].…”

Section: A Related Workmentioning

confidence: 77%

Performances Enhancement of Fingerprint Recognition System Using Classifiers

et al. 2019

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Fingerprint recognition is best known and generally used as a biometric technology because of their high acceptability, immutability, and uniqueness. A fingerprint consists of ridges and valleys pattern also known as furrows. These patterns fully develop in the mother's womb and remain constant throughout the whole lifetime of the individual. The ridge bifurcation and ridge termination are the main minutiae features that are extracted for identification of individuals in fingerprint recognition system. The aim of this paper is to enhance the performance of the fingerprint recognition systems using classifiers. To achieve the aim, fingerprints from the FV2002 database are used, before these fingerprints are evaluated, image enhancement and binarization is applied as a pre-processing on fingerprints, by combining many methods to build a database of fingerprint features having minutia marking and minutia feature extraction. The fingerprint recognition is presented by image classification using MATLAB classifiers, i.e., Decision Tree, Linear Discriminant Analysis, medium Gaussian support vector machine (MG-SVM), fine K-nearest neighbor, and bagged tree ensemble. The aim of this paper is to make a comparison between classifiers for performance enhancement of the fingerprint recognition system. The MG-SVM classifiers significantly give the highest verification rate of 98.90% among all classifies used.

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Section: A Related Workmentioning

confidence: 77%

Performances Enhancement of Fingerprint Recognition System Using Classifiers

et al. 2019

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“…Haiyun Xu et al [6] design a system for improving matching speed by compressing spectral minutiae feature using Column PCA (Principal Component Analysis) and Line DFT (Line Discrete Fourier Transform) reduction techniques. De-Song Wang et al [7] presented a Fingerprint based authentication system with Smartphone's. This scheme is better for computational complexity with Khan's and Yoon-Yoo's scheme.…”

Section: A Matching Techniques and Recognition Methodsmentioning

confidence: 99%

A Review on Biometrics Authentication System Using Fingerprint

Sureshbabu

Sakthivadivu

2019

AJCST

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Technology based on Biometric identification and verification is one leading research area. It deals with the concept analyzing the human body characteristics through Biometric devices for various authentications process. There are many Biometric authentication systems are available for verification process. This paper discusses the role of Fingerprint authentication. FP recognition is highly used biometric technique, because of abundance sources (i.e. ten fingers) availability for collecting data. Discussion on Fingerprint matching techniques, recognition methods and their performance analysis are made in the paper.

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“…Jing Luo et al [27] [35] comprehend a concept for FR using digital camera. The Gabor features obtained by the Gabor filters are compressed using PCA and then matching is performed with the help of cosine angle.…”

Section: A Matching Techniques and Recognition Methodsmentioning

confidence: 99%

A Study of Biometric Approach Using Fingerprint Recognition

Subban¹,

Mankame²

2013

LNSE

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Abstract-Fingerprint (FP) serves to identify that the person authenticating is who he/she claims to be. FP identification is popular biometric technique due to easiness in acquiring, availability of plenty sources (i.e. ten fingers) for collecting data and their established use. This paper summarizes the research work carried out in FP matching techniques, recognition methods and their performance analysis.

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An Improved Fingerprint Recognition Algorithm Using EBFNN

Cited by 14 publications

References 9 publications

Performances Enhancement of Fingerprint Recognition System Using Classifiers

Performances Enhancement of Fingerprint Recognition System Using Classifiers

A Review on Biometrics Authentication System Using Fingerprint

A Study of Biometric Approach Using Fingerprint Recognition

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