A Support Vector Machine Approach for Truncated Fingerprint Image Detection from Sweeping Fingerprint Sensors

Chen, Chi-Jim; Pai, Tun-Wen; Cheng, Mox

doi:10.3390/s150407807

Cited by 4 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As basic deformations, it is suggested to use the "principal" deformations obtained by the main component method on the reference fingerprint array [21,22]. To calculate the main components, three FVC2002 databases were used, containing images from optical and capacitive fingerprint scanners [23,24]. Each database contains 8 samples for 100 people, i.e.…”

Section: Resultsmentioning

confidence: 99%

Development modeling methods of analysis and synthesis of fingerprint deformations images

AlKaraawi

Dhahir

Alameri³

et al. 2020

IJECE

View full text Add to dashboard Cite

The current study is to develop modeling methods, Analysis and synthesis of fingerprints deformations images and their application in problems of automatic fingerprint identification. In the introduction justified urgency of the problem, is given a brief description of thematic publications. In this study will review of modern technologies of biometric technologies and methods of biometric identification, the review of fingerprint identification systems, investigate for distorting factors. The influence of deformations is singled out, the causes of deformation of fingerprints are analyzed. The review of modern ways of the account and modeling of deformations in problems of automatic fingerprint identification is given. The scientific novelty of the work is the development of information technologies for the analysis and synthesis of deformations of fingerprint images. The practical value of the work in the application of the developed methods, algorithms and information technologies in fingerprints identification systems. In addition, it has been found that our paper "devoted to research methods and synthesis of the fingerprint deformations" is a more appropriate choice than other papers.

show abstract

Section: Resultsmentioning

confidence: 99%

Development modeling methods of analysis and synthesis of fingerprint deformations images

AlKaraawi

Dhahir

Alameri³

et al. 2020

IJECE

View full text Add to dashboard Cite

show abstract

“…The selection of the kernel function will affect the precision of the SVM [ 32 ]. Until now, there is no effective method to select an optimal kernel function for a particular question.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

A two-stage classification method for borehole-wall images with support vector machine

et al. 2018

View full text Add to dashboard Cite

Analyzing geological drilling hole images acquired by Axial View Panoramic Borehole Televiewer (APBT) is a key step to explore the geological structure in a geological exploration. Conventionally, the borehole images are examined by technicians, which is inefficient and subjective. In this paper, three dominant types of borehole-wall images on coal-rock mass structure, namely, border images, fracture images and intact rock mass images are mainly studied. The traditional image classification methods based on unified feature extraction algorithm and single classifier is not effect for the borehole images. Therefore, this paper proposes a novel two-stage classification approach to improve the classification performance of borehole images. In the first-stage classification, the border images are identified from three kinds of images based on texture features and gray-scale histograms features. For the remaining two types of images, in the second-stage classification, Gabor filter is first applied to segment the region of interest (ROI) (such as microfracture, absciss layer and horizontal cracks, etc.) and the central interference region. Then, using the same feature vector after eliminating the central interference region, fracture images are separated from intact rock mass images. We test our two-stage classification system with real borehole images. The results of experimental show that the two-stage classification method can effectively classify three major borehole-wall images with the correction rate of 95.55% in the first stage and 95% in the second stage.

show abstract

“…The overall accuracy achieved though was still limited as accurate singular point detection on fingerprint was still unstable at the time. Chen et al [24] proposed to use a SVM as a filter to identify and reject the truncated fingerprint images, thus improving the accuracy of the proposed fingerprint authentication system to 90.7%. Alias [25] presented an SVM-based fingerprint classification model that achieved accuracy of 94.7%.…”

Section: Related Workmentioning

confidence: 99%

A Privacy-Preserving Biometric Authentication System With Binary Classification in a Zero Knowledge Proof Protocol

Tran

Turnbull

Wang

et al. 2022

IEEE Open J. Comput. Soc.

View full text Add to dashboard Cite

Biometric authentication is, over time, becoming an indispensable complementary component to traditional authentication methods that use passwords and tokens. As a result, the research interest in the protection techniques for the biometric template has also grown considerably. In this paper, we present a light-weight AI-based biometric authentication that operates based on the binary representation of a biometric instance. In details, a binary classifier will be trained using the binary strings that represent the intraclass and interclass biometric subjects. The Support Vector Machine and Multi-layer Perceptron Neural Network are chosen as the classifier to evaluate the fingerprint-based and iris-based authentication capability. Afterward, the authenticated biometric string is fed to a hash function to produce a hash value, which is to be used in a Zero-Knowledge-Proof Protocol for the purpose of privacy preservation. In order to improve the recognition of the classifier, we devise a simple yet efficient strategy to enhance the discriminativeness of the binary strings and name it the Composite Features Retrieval. We evaluated the proposed method with the four publicly available fingerprint datasets FVC2002-DB1, FVC2002-DB2, FVC2002-DB3, and FVC2004-DB2 and the iris dataset UBIRISv1. The promising performance shows this method's capability.

show abstract

A Support Vector Machine Approach for Truncated Fingerprint Image Detection from Sweeping Fingerprint Sensors

Cited by 4 publications

References 16 publications

Development modeling methods of analysis and synthesis of fingerprint deformations images

Development modeling methods of analysis and synthesis of fingerprint deformations images

A two-stage classification method for borehole-wall images with support vector machine

A Privacy-Preserving Biometric Authentication System With Binary Classification in a Zero Knowledge Proof Protocol

Contact Info

Product

Resources

About