Development of an efficient secure biometric system by using iris, fingerprint, face

Telgad, Rupali L.; Siddiqui, Almas; Lothe, Savita A.; Deshmukh, Prapti D.; Jadhao, Gajanan

doi:10.1109/icisim.2017.8122156

Cited by 3 publications

(1 citation statement)

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“…Similarly, a biometric security system using fingerprint modality was also proposed for Mobile Template protection by Yang et al [31]. Telgad et al [27] also proposed a security system using iris, fingerprint, and face modalities. All these systems provide an efficient privilege and this will increase the demand of the biometric systems for security.…”

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confidence: 99%

EAgeBioS: Enhanced Biometric System to handle the Effects of Template Aging

Kumar*,

Lamba,

Jangra

2019

IJITEE

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Biometric Systems are well-known security systems that can be used anywhere for authentication, authorization or any kind of security verifications. In biometric systems, the samples are trained first and then it can be used for testing in long runs. Many recent researches have shown that a biometric system may fail or get compromised because of the aging of the biometric templates. The fact that temporal duration affects the performance of the biometric system has shattered the belief that iris does not change over lifetime. This is also possible in the case of iris. So, the main focus of this work is to analyze the effect of aging and also to propose a new system that can deal with template aging. We have proposed a new iris recognition system with an image enhancement mechanism and different feature extraction mechanisms. In this work, three different features are extracted, which are then fused to be used as one. The full system is trained on a dataset of 2500 samples for the year 2008 and testing is done in three different phases (i) No-Lapse, (ii) 1-Year Lapse and (iii) 2-Year Lapse. A portion of the ND-Iris-Template-Aging dataset [11] is used with a period of three years lapse. Results show that the performance of the hybrid classifier AHyBrK [17] is improved as compared to KNN and ANN and the effect of aging in terms of degraded performance is clear. The performance of this system is measured in terms of False Rejection Rate, Error Rate, and Accuracy. The overall performance of AHyBrK is 51.04% and 52.98% better than KNN and ANN respectively in terms of False Rejection Rate and Error Rate whereas the accuracy of thisproposed system is also improved by 5.52% and 6.04% as compared to KNN and ANN respectively. This proposed system also achieved high accuracy for all the test phases.

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confidence: 99%