Multimodal biometric authentication based on score level fusion of finger biometrics

Peng, Jialiang; El‐Latif, Ahmed A. Abd; Li, Qiong; Niu, Xiamu

doi:10.1016/j.ijleo.2014.07.027

Cited by 101 publications

(44 citation statements)

References 31 publications

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“…In transformation based fusion, the matching scores from different modalities are transformed/normalized into same domain and then combined using various fusion rules like product, sum etc. In classifier based fusion, the scores from various modalities are combined and given as an input to classifier and lastly Density based fusion which is based on estimation of matching scores density either by parametric or non-parametric methods [5,19,31]). In present work, transformation based fusion is employed.…”

Section: Matching Score Level Fusionmentioning

confidence: 99%

“…These different biometric evidences acquired from various sources can be merged at feature level, score level or decision level. At feature level, the features of different biometric traits are integrated., at score level, the matching scores of different traits from different matchers are combined while in decision level, output of different traits (final identity or result of verification) are considered for final output [3,4,5]. The proper integration of such information from different modalities can be very helpful than using information from just single modality.…”

Section: Introductionmentioning

confidence: 99%

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Wavelet based Multimodal Biometrics with Score Level Fusion Using Mathematical Normalization

Sanjekar¹,

Patil²

2019

IJIGSP

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Biometric based authentication is playing a very important role in various security related applications. A novel multimodal biometric verification based on fingerprint, palmprint and iris with matching score level fusion using Mathematical Normalization is proposed in this paper. In feature extraction stage of unimodal, features of each modality are extracted by applying wavelet decomposition using 6 different wavelet families and 35 respective wavelet family members. Further, the three optimal combinations of unimodal systems based on equal error rate achieved by wavelet(s) are chosen for development of multimodal biometric system. In matching score level fusion, along with wellknown normalization techniques-Min-max, Tan-h and Zscore, the performance of multimodal systems are also analyzed using Mathematical Normalization (Math-norm) followed by product, weighted product, sum and average fusion rule. The experiments are conducted on database of 100 different subjects from publically available FVC2006, CASIA V1 and IITD database of fingerprint, palmprint and iris, respectively. The experimental results clearly show that Mathematical Normalization followed by weighted product has given promising accuracy with equal error rate (EER) of 0.325%.

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Section: Matching Score Level Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wavelet based Multimodal Biometrics with Score Level Fusion Using Mathematical Normalization

Sanjekar¹,

Patil²

2019

IJIGSP

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“…In reliefF feature selection algorithm, the quality estimation [ ] is updated by utilizing the Eq. (11), (12), and (13).…”

Section: Feature Selection Using Modified Relieff Feature Selectionmentioning

confidence: 99%

“…J. Peng, A.A.A. El-Latif, Q. Li, and X. Niu, [12] developed an effective finger based multimodal biometric authentication system that combines fingerprint, finger knuckle, finger vein, and finger shape features of an individual human finger. In addition, the developed system utilized score level fusion approach on the basis of the triangular norm with forefinger biometric traits.…”

Section: Literature Reviewmentioning

confidence: 99%

Multimodal Biometric Authentication System Using Modified ReliefF Feature Selection and Multi Support Vector Machine

Gavisiddappa¹,

Mahadevappa²,

Patil³

2020

IJIES

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In the present scenario, multimodal biometric authentication is one of the emerging fields, which is applied in different applications like prison security, criminal identification, banking security, etc. The objective of this research work is to develop an effective feature selection algorithm to determine the optimal feature values for further improving the performance of multimodal biometric authentication. Initially, the input images were collected from Chinese Academy of Science Institute for Automation (CASIA) dataset. Then, feature extraction was carriedout by using Local Binary Pattern (LBP), minutiae feature extraction, Histogram of Oriented Gradient (HOG), and Gray-Level Co-Occurrence Matrix (GLCM) features like cluster prominence, Inverse Difference Moment Normalized (IDMN), and autocorrelation. After feature extraction, modified reliefF feature selection algorithm was used for rejecting the irrelevant features or for choosing the optimal features. In modified algorithm, Chebyshev distance measure was utilized instead of Manhattan distance in order to find the nearest miss and nearest hit instances. At last, the optimal feature values were given as the input for Multi-Support Vector Machine (MSVM) classifier for classifying an individual as an authorized or unauthorized person. The experimental result showed that the proposed system improved the classification accuracy up to 7% as related to the existing systems in terms of accuracy.

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“…Source: own work based on: Gaikwad & Pasalkar (2004), Peng et al (2014) and Venkatraman & Delpachitra (2008).…”

Section: Signature Recognitionmentioning

confidence: 99%

Prospects for the application of biometrics in the Polish banking sector

Piotrowska

Polasik

Piotrowski

2017

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Research background: The ongoing digitisation process in the banking sector, coupled with widespread remote provision of services, is leading to the advent of new solutions in the field of broadly understood security. The increasingly sophisticated forms of attacks on banks’ IT systems and their users have engendered the need to implement authentication methods that would ensure high security levels, but would also be convenient for banks' clients and suited to the requirements of mass service. Biometric technology seems to be a solution to this issue. The two factors that may boost its proliferation are: the fact that banks need to adjust to more rigid regulations, and technological advancements leading to cost reduction and increased availability of biometric solutions in mobile devices. Purpose of the article: The purpose of the article is to assess the prospects for the application of biometrics by the banking sector in Poland in individual customer service channels. Methods: The basis for theoretical considerations comprises the analysis of literature on authentication techniques and research on the processes whereby consumers accept new technologies. The empirical part of the paper was based on the results of the authors’ questionnaire survey among representatives of the Polish banking sector. Findings & value added: The banking sector in Poland is on the verge of a sweeping bio-metric revolution in the coming years, because most traditional identity verification and authorisation methods currently available in banking do not comply with strict security and user convenience requirements and RTS regulations. Biometric technologies will be of use in all customer service channels, with the experts indicating authentication in bank branches, ATMs, and mobile banking as the primary implementation areas. Solutions which are most likely to be applied are those based on biometrics of fingerprints and finger veins, as well as voice biometrics.

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Multimodal biometric authentication based on score level fusion of finger biometrics

Cited by 101 publications

References 31 publications

Wavelet based Multimodal Biometrics with Score Level Fusion Using Mathematical Normalization

Wavelet based Multimodal Biometrics with Score Level Fusion Using Mathematical Normalization

Multimodal Biometric Authentication System Using Modified ReliefF Feature Selection and Multi Support Vector Machine

Prospects for the application of biometrics in the Polish banking sector

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