A Multimodal Biometric System for Secure User Identification Based on Deep Learning

Arora, Shefali; Bhatia, M. P. S.; Kukreja, Harshita

doi:10.1007/978-981-15-5856-6_8

Cited by 13 publications

(10 citation statements)

References 19 publications

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“…Author [18] Arora proposed a multimodal biometric system using the fusion of features of iris and face. Multiple pre-trained models are used for feature extraction.…”

Section: Review Of Existing Iris Presentation Attacks Detection Methodsmentioning

confidence: 99%

Detection of Iris Presentation Attacks Using Hybridization of Discrete Cosine Transform and Haar Transform With Machine Learning Classifiers and Ensembles

Khade

Gite²,

Thepade³

et al. 2021

IEEE Access

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Iris biometric identification allows for contactless authentication, which helps to avoid the transmission of diseases like COVID-19. Biometric systems become unstable and hazardous due to spoofing attacks involving contact lenses, replayed video, cadaver iris, synthetic Iris, and printed iris. This work demonstrates the iris presentation attacks detection (Iris-PAD) approach that uses fragmental coefficients of transform iris images as features obtained using Discrete Cosine Transform (DCT), Haar Transform, and hybrid Transform. In experimental validations of the proposed method, three main types of feature creation are investigated. The extracted features are utilized for training seven different machine learning classifiers alias Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), and decision tree(J48) with ensembles of SVM+RF+NB, SVM+RF+RT, and RF+SVM+MLP (multi-layer perceptron) for proposed iris liveness detection. The proposed iris liveness detection variants are evaluated using various statistical measures: accuracy, Attack Presentation Classification Error Rate (APCER), Normal Presentation Classification Error Rate (NPCER), Average Classification Error Rate (ACER). Six standard datasets are used in the investigations. Total nine iris spoofing attacks are getting identified in the proposed method. Among all investigated variations of proposed iris-PAD methods, the 4 x 4 of fragmental coefficients of a Hybrid transformed iris image with RF algorithm have shown superior iris liveness detection with 99.95% accuracy. The proposed hybridization of transform for features extraction has demonstrated the ability to identify all nine types of iris spoofing attacks and proved it robust. The proposed method offers exceptional performances against the Synthetic iris spoofing images by using a random forest classifier. Machine learning has massive potential in a similar domain and could be explored further based on the research requirements. INDEX TERMS Iris presentation attacks, liveness detection, Haar transformation, DCT, hybrid transform I.

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“…Author [18] Arora proposed a multimodal biometric system using the fusion of features of iris and face. Multiple pre-trained models are used for feature extraction.…”

Section: Review Of Existing Iris Presentation Attacks Detection Methodsmentioning

confidence: 99%

Detection of Iris Presentation Attacks Using Hybridization of Discrete Cosine Transform and Haar Transform With Machine Learning Classifiers and Ensembles

Khade

Gite²,

Thepade³

et al. 2021

IEEE Access

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“…"FAR = 0.1%, which means that in 1 out of 1000 cases, a biometric iris detection system has a probability of granting access to an unauthorized individual" [97].…”

Section: Performances Measures (Rq4)mentioning

confidence: 99%

“…When the two systems are compared, the more precise "one shows lower FRR at the same level of FAR" [97].…”

Section: B Frr (False Rejection Rate)mentioning

confidence: 99%

Iris Liveness Detection for Biometric Authentication: A Systematic Literature Review and Future Directions

et al. 2021

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Biometrics is progressively becoming vital due to vulnerabilities of traditional security systems leading to frequent security breaches. Biometrics is an automated device that studies human beings’ physiological and behavioral features for their unique classification. Iris-based authentication offers stronger, unique, and contactless identification of the user. Iris liveness detection (ILD) confronts challenges such as spoofing attacks with contact lenses, replayed video, and print attacks, etc. Many researchers focus on ILD to guard the biometric system from attack. Hence, it is vital to study the prevailing research explicitly associated with the ILD to address how developing technologies can offer resolutions to lessen the evolving threats. An exhaustive survey of papers on the biometric ILD was performed by searching the most applicable digital libraries. Papers were filtered based on the predefined inclusion and exclusion criteria. Thematic analysis was performed for scrutinizing the data extracted from the selected papers. The exhaustive review now outlines the different feature extraction techniques, classifiers, datasets and presents their critical evaluation. Importantly, the study also discusses the projects, research works for detecting the iris spoofing attacks. The work then realizes in the discovery of the research gaps and challenges in the field of ILD. Many works were restricted to handcrafted methods of feature extraction, which are confronted with bigger feature sizes. The study discloses that dep learning based automated ILD techniques shows higher potential than machine learning techniques. Acquiring an ILD dataset that addresses all the common Iris spoofing attacks is also a need of the time. The survey, thus, opens practical challenges in the field of ILD from data collection to liveness detection and encourage future research.

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“…Considering its positive aspects of greater identity validation and security, it is expected that the frequency of use of biometrics will increase in the near future. Successful machine learning and deep learning models are available in the literature, especially on the use of biometrics (Adamović et al, 2020;Arora et al, 2020;Pandey et al, (2017); Sundararajan & Woodard, 2018).…”

Section: Use Of Biometric Properties In the Security Areamentioning

confidence: 99%

Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data

Güzel¹

2021

JPLC

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Young adults engage in a disproportionately high rate of problematic behaviors such as risky sexual activities, academic dishonesty, and substance abuse. In order to understand why this occurs, two lesser-known constructs related to risktaking behavior were investigated in this study: differential identification, which has yet to be empirically studied in the context of emerging adults, and criminogenic thinking, which has only been evaluated in this context to a minimal degree. To bridge the gap between these two constructs, motivation was hypothesized to be a moderator in each of these respective relationships. Data from 309 emerging adults were recruited via Amazon's Mechanical Turk (MTurk) to complete the survey materials online. The analysis consisted of a hierarchical multiple linear regression. Results indicated that motivation did not moderate either relationship (i.e., differential identification and risk-taking behavior and criminogenic thinking and risk-taking behavior). However, a main effect was found between one aspect of differential identification and risk-taking behavior. No other main effects were found in this study. Potential explanations for the results of this study are discussed, as well as implications and directions for future research.

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A Multimodal Biometric System for Secure User Identification Based on Deep Learning

Cited by 13 publications

References 19 publications

Detection of Iris Presentation Attacks Using Hybridization of Discrete Cosine Transform and Haar Transform With Machine Learning Classifiers and Ensembles

Detection of Iris Presentation Attacks Using Hybridization of Discrete Cosine Transform and Haar Transform With Machine Learning Classifiers and Ensembles

Iris Liveness Detection for Biometric Authentication: A Systematic Literature Review and Future Directions

Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data

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