Face–Iris Multimodal Biometric Identification System

Ammour, Basma; Boubchir, Larbi; Bouden, Toufik; Ramdani, Messaoud

doi:10.3390/electronics9010085

Cited by 82 publications

(48 citation statements)

References 42 publications

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“…Banking and Finance sectors use biometrics for authentication [1,6].Biometrics systems are used to secure medical records. Biometrics recognition systems are used in many areas such as passport verification, airports, buildings, mobile phones, identity cards [9].…”

Section: Applications Of Biometric Systemsmentioning

confidence: 99%

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Multi-Modal Biometrics Systems: Concepts, Strengths, Challenges and Solutions

2021

IJATCSE

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Authentication is a very important aspect of computer security. Most systems employ strategies such as Password-based authentication, Multi-factor authentication, Certificate based authentication which are accompanied with a lot ofchallenges. To address this issue, most security systems have introduced the use of biometrics for authentication. Unimodal biometrics systems have many limitations regarding performance and accuracy. The use of Multimodal biometrics systems for authentication is recently attracting the attention of researchers due to its capacity to overcome most of the drawbacks of Unimodal biometric systems. This paper focuses on the use of biometric technology for authentication. The strengths of multimodal biometric systems, together with the challenges of multimodal biometric systems are presented. The paper also suggests solutions to the challenges of multimodal biometric systems.

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Section: Applications Of Biometric Systemsmentioning

confidence: 99%

“…Using this score, the decision making module either confirms or denies a person's claimed identity. Unimodal biometric systems are often affected by the following limitations [2,3,4,7,8,9,10,11,12,13]. a.…”

Section: Unimodal Biometric Systemsmentioning

confidence: 99%

Multi-Modal Biometrics Systems: Concepts, Strengths, Challenges and Solutions

2021

IJATCSE

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“…The proposed system achieved an accuracy of 97.33%. Furthermore, Ammour et al [ 9 ] proposed a new feature extraction technique for a multimodal biometric system that relayed on face and iris traits. The iris feature extraction was carried out with a multi-resolution 2D Log-Gabor filter.…”

Section: Related Workmentioning

confidence: 99%

“…Several biometrics researchers have relied on machine learning algorithms for recognition purposes [ 5 , 6 , 7 , 8 , 9 ]. Machine learning algorithms require some extraction techniques to extract features from raw biometric data and transform the raw data into an appropriate format before classifying it.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approach for Multimodal Biometric Recognition System Based on Fusion of Iris, Face, and Finger Vein Traits

Alay¹,

Al-Baity

2020

Sensors

137

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With the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our everyday life. In this regard, multimodal biometrics technology has gained interest and became popular due to its ability to overcome a number of significant limitations of unimodal biometric systems. In this paper, a new multimodal biometric human identification system is proposed, which is based on a deep learning algorithm for recognizing humans using biometric modalities of iris, face, and finger vein. The structure of the system is based on convolutional neural networks (CNNs) which extract features and classify images by softmax classifier. To develop the system, three CNN models were combined; one for iris, one for face, and one for finger vein. In order to build the CNN model, the famous pertained model VGG-16 was used, the Adam optimization method was applied and categorical cross-entropy was used as a loss function. Some techniques to avoid overfitting were applied, such as image augmentation and dropout techniques. For fusing the CNN models, different fusion approaches were employed to explore the influence of fusion approaches on recognition performance, therefore, feature and score level fusion approaches were applied. The performance of the proposed system was empirically evaluated by conducting several experiments on the SDUMLA-HMT dataset, which is a multimodal biometrics dataset. The obtained results demonstrated that using three biometric traits in biometric identification systems obtained better results than using two or one biometric traits. The results also showed that our approach comfortably outperformed other state-of-the-art methods by achieving an accuracy of 99.39%, with a feature level fusion approach and an accuracy of 100% with different methods of score level fusion.

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“…Beberapa penelitian sebelumnya telah mengembangkan metode identifikasi wajah berbasis filter Gabor. Seperti pada penelitian dengan pembahasan keefektifan dari metode identifikasi wajah menggunakan supervised classifier [6], akurasi dari implementasi metode Gabor dan artificial neural network [7], Anisotropic Diffusion sebagai preprocessing dan filter Gabor untuk ekstraksi fitur dengan berbagai pose, pencahayaan, dan ekspresi [8], Adaptive Histogram Equalization (AHE) untuk meningkatkan akurasi dari face recognition [9], dan penggunaan filter Gabor untuk multi ekstraksi [10].…”

Section: Pendahuluanunclassified