Multimodal Biometric Cryptosystem for Face and Ear Recognition Based on Fuzzy Vault

Amirthalingam, Gandhimathi; Radhamani, G.

doi:10.19026/rjaset.7.791

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…However, the real challenge is to find biometrics data which is rich in distinctive features [4]. The ear biometric is a kind of image processing based biometrics technology, which has many unique features that can be extracted and used as a biometric trait to identify or/and verify people.…”

Section: Ear Biometricsmentioning

confidence: 99%

Evaluation of Voice & Ear Biometrics Authentication System

Mohammed¹,

Hegarty²

2017

IJEME

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The core aim of biometrics authentication methods and technologies is to solve issues and concerns existing in traditional authentication methods like passwords, PIN numbers or identity cards. The Most important concern for business sectors and organizations is to authenticate individuals who interact with them and their services. By considering more than one biometric technology, the authentication process is expected to be more reliable and secure. The Ear and Voice Multimodal Biometrics System is evaluated to compare its performance with ear and voice unimodal systems; the multimodal system takes advantages of the permanence characteristic of ear biometric and voice biometric which is highly acceptable by users. According to the experiment, the ear and voice multimodal system provides better performance than the ear or voice unimodal system. In addition to that, the multimodal system makes a right balance between false rejection and false acceptance rates. This evaluation is intended to contribute to multimodal biometrics research by using behavioral biometric (voice print) and physiological biometric (ear-print) and makes advantage of using both of them in one system.

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Section: Ear Biometricsmentioning

confidence: 99%

Evaluation of Voice & Ear Biometrics Authentication System

Mohammed¹,

Hegarty²

2017

IJEME

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“…The research on 3D face recognition techniques is increasing due to the accessibility of advanced 3D imaging devices and their fast computational process. Three-dimensional images of the facial surfaces are acquired for authentication reasons [ 19 – 21 ]. Three-dimensional facial images have a couple of focal points on 2D facial images; it helps to mark straight edges in 3D spacing [ 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Score-Level Fusion of 3D Face and 3D Ear for Multimodal Biometric Human Recognition

Tharewal

Malche

Tiwari

et al. 2022

Computational Intelligence and Neuroscience

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A novel multimodal biometric system is proposed using three-dimensional (3D) face and ear for human recognition. The proposed model overcomes the drawbacks of unimodal biometric systems and solves the 2D biometric problems such as occlusion and illumination. In the proposed model, initially, the principal component analysis (PCA) is utilized for 3D face recognition. Thereafter, the iterative closest point (ICP) is utilized for 3D ear recognition. Finally, the 3D face is fused with a 3D ear using score-level fusion. The simulations are performed on the Face Recognition Grand Challenge database and the University of Notre Dame Collection F database for 3D face and 3D ear datasets, respectively. Experimental results reveal that the proposed model achieves an accuracy of 99.25% using the proposed score-level fusion. Comparative analyses show that the proposed method performs better than other state-of-the-art biometric algorithms in terms of accuracy.

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