Hybridization of Bimodal Biometrics for Access Control Authentication

Zuva, Tranos

doi:10.7763/ijfcc.2014.v3.344

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Experimental results show the effectiveness of the proposed algorithm. Zuva et al, (2014) proposed using both fingerprint and face for authentication in access system. The study integrated fingerprint and face biometric to improve the performance in access control system.…”

Section: IImentioning

confidence: 99%

Development of an Improved Access Control System using an Enhanced Bimodal Crypto-Biometric System

O. O,

A. B.,

et al. 2023

IJRASET

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Recently, biometric was being integrated with cryptography (crypto-biometric system) to alleviate the limitations of the biometric or cryptography system. However, the main shortcoming of cryptography is poorly-chosen or forgotten password while challenges with biometrics include interclass similarities in the feature sets used to represent traits. In this work, a combination of cryptography and bimodal biometric was developed, an Advanced Encryption Standard based Fast Fourier Transform (AES-FFT) was developed and used as the cryptography technique. Hence, an attempt was made to develop an improved access control system using an enhanced bimodal bio-cryptography. Biometric features was extracted from individual face and iris after application of suitable preprocessing techniques for each modality using Principal Component Analysis (PCA) while cryptography key was generated using fused features from the face and iris by Advanced Encryption System based Fast Fourier Transform (AES-FFT). The two captured biometric data at acquisition module via webcam were subjected to appropriate pre-processing and feature extraction module. The features extracted were fused at feature level using weighted average and optimal features were selected using genetic programming (GP). The classification technique used was Support Vector Machine (SVM).

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Section: IImentioning

confidence: 99%

Development of an Improved Access Control System using an Enhanced Bimodal Crypto-Biometric System

O. O,

A. B.,

et al. 2023

IJRASET

View full text Add to dashboard Cite

show abstract

“…The authors [12] integrated fingerprint and face biometric to improve performance in an access control system. They considered restoration of distorted and misaligned fingerprints caused by environmental noise such as oil, wrinkles, dry skin, dirt, displacement etc.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Multimodal Biometric System Based on Convolutional Neural Network

OMOTOSHO¹,

Ogundoyin²,

Adebayo³

et al. 2021

JESR

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Multimodal biometric system combines more than one biometric modality into a single method in order, to overcome the limitations of unimodal biometrics system. In multimodal biometrics system, the utilization of different algorithms for feature extraction, fusion at feature level and classification often to complexity and make fused biometrics features larger in dimensions. In this paper, we developed a face-iris multimodal biometric recognition system based on convolutional neural network for feature extraction, fusion at feature level, training and matching to reduce dimensionality, error rate and improve the recognition accuracy suitable for an access control. Convolutional Neural Network is based on deep supervised learning model and was employed for training, classification, and testing of the system. The images are preprocessed to a standard normalization and then flow into couples of convolutional layers. The developed multimodal biometrics system was evaluated on a dataset of 700 iris and facial images, the training database contain 600 iris and face images, 100 iris and face images were used for testing. Experimental result shows that at the learning rate of 0.0001, the multimodal system has a performance recognition accuracy (RA) of 98.33% and equal error rate (ERR) of 0.0006%.

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Hybridization of Bimodal Biometrics for Access Control Authentication

Cited by 2 publications

References 8 publications

Development of an Improved Access Control System using an Enhanced Bimodal Crypto-Biometric System

Development of an Improved Access Control System using an Enhanced Bimodal Crypto-Biometric System

An Enhanced Multimodal Biometric System Based on Convolutional Neural Network

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