Human Authentication Using Face and Fingerprint Biometrics

Darwish, Ashraf; Zaki, W Mimi Diyana W; Saad, Omar M.; Nassar, Nadia M.; Schaefer, Gerald

doi:10.1109/cicsyn.2010.40

Cited by 18 publications

(5 citation statements)

References 12 publications

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“…Wehavegonethroughseveralstudieswhichresembletheproposedwork;someoftheprominent workwhichresemblesourworkisdiscussedinthissection.Authors (Jain,Ross,&Prabhakar,2004) presented comparative approach among different traits and presents error rates in terms of False matchedrate(FMR)andFalseNon-MatchedRate(FNMR).Inthenextstudyauthors (Darwish, Zaki, Saad, Nassar, & Schaefer, 2010) proposed an effective approach to multimodal biometrics usingfaceandfingerprintrecognitionwhereFacialimagerepresentationisperformedusinglocal binarypattern(LBP)textures,whilefingerprintsarerecognisedonthebasisofminutiaeextraction.…”

Section: Prior Published Studiesmentioning

confidence: 99%

Multimodal Biometric Based on Fusion of Ridge Features with Minutiae Features and Face Features

Singh

Khanna

Garg

2020

International Journal of Information System Modeling and Design

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Multimodal biometrics refers to the exploiting combination of two or more biometric modalities in an identification of a system. Fingerprint, face, retina, iris, hand geometry, DNA, and palm print are physiological traits while voice, signature, keystrokes, gait are behavioural traits used for identification by a system. Single biometric features like faces, fingerprints, irises, retinas, etc., deteriorate or change with time, environment, user mode, physiological defects, and circumstance therefore integrating multi features of biometric traits increase robustness of the system. The proposed multimodal biometrics system presents recognition based on face detection and fingerprint physiological traits. This proposed system increases the efficiency, accuracy and decreases execution time of the system as compared to the existing systems. The performance of proposed method is reported in terms of parameters such as False Rejection Rate (FRR), False Acceptance Rate (FAR) and Equal Error Rate (EER) and accuracy is reported at 95.389%.

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Section: Prior Published Studiesmentioning

confidence: 99%

Multimodal Biometric Based on Fusion of Ridge Features with Minutiae Features and Face Features

Singh

Khanna

Garg

2020

International Journal of Information System Modeling and Design

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“…The features of the biometrics are extracted respectively by using eigenface, eigenear and eigensignature. Darwish et al [45] proposed a multimodal biometric system depending on the face and fingerprint fusion biometrics. The face recognition was done by uniform local binary patterns (ULBP), while fingerprint recognition was performed by minutiae extraction.…”

Section: Related Workmentioning

confidence: 99%

A Personal Identification Framework based on Facial Image and Fingerprint Fusion Biometric

ElAlami¹,

Amin²,

El-alfi³

2012

IJCA

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Biometric based person identity verification is gaining more and more attention. Several studies have shown that multimodal biometric identification systems improve the recognition accuracy and reliability compared with recognition using a single biometric. The present paper introduces a new personal identification framework that is based on the fusion of face and fingerprint biometrics. The proposed framework overcomes the limitations of face recognition systems as well as fingerprint verification systems. The gray-level co-occurrence matrix and the minutiae extraction are used to represent the features of face and fingerprint image respectively. This framework uses the correlation coefficient as a similarity measure to retrieve the closest face and the corresponding fingerprint images with a query image. Experimental results performed on a given database of face and fingerprint images show that the proposed framework improved greatly the security and recognition rate.

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“…RELATED WORKS Darwish, A.A. et al, [2] proposed an idea of human authentication using fingerprint and face recognition. In this paper, the multimodal biometric system has been proposed for the fusion of face and fingerprint biometrics.…”

Section: Iintroductionmentioning

confidence: 99%

Efficient personal identification using multimodal biometrics

Ahilandeswari¹,

Prabu²,

Priyadharshini³

et al. 2015

2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015]

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For recent years, the use of personal identity systems using multimodal biometrics has been increasing tremendously in number of fields. Although the Unimodal biometric systems serve well in various areas, it is notable that they have disadvantages regarding security and accuracy. Multimodal biometric systems focus on combining more than one possible biometric technology in order to secure the applications to a great extent. This in turn resulted in the improvement of performance and robustness against fraudulent attacks. Personal identification plays a major role in any information sharing process. For this, the identification systems must be designed in such a manner that it should minimize the system error rates, susceptibility mimics and false match rate. Considering these factors, we aim at providing a new personal identification system using multimodal biometrics. The proposed system uses three of the biometric technologies in the process of identification: (i) Face recognition; (ii) Fingerprint recognition; and (iii) Speech Recognition. Here we discuss about the methods of feature extraction, fusion and decision used in our system and their advantages over other biometric systems.

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Human Authentication Using Face and Fingerprint Biometrics

Cited by 18 publications

References 12 publications

Multimodal Biometric Based on Fusion of Ridge Features with Minutiae Features and Face Features

Multimodal Biometric Based on Fusion of Ridge Features with Minutiae Features and Face Features

A Personal Identification Framework based on Facial Image and Fingerprint Fusion Biometric

Efficient personal identification using multimodal biometrics

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