An Overview of Deep Learning Techniques for Biometric Systems

Almabdy, Soad; Elrefaei, Lamiaa A.

doi:10.1007/978-3-030-51920-9_8

Cited by 8 publications

(4 citation statements)

References 58 publications

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“…Facial and vocal biometrics are non-invasive and thus are more suitable for banking services among these. The advantages of deep neural networks for biometric feature extraction have been verified in the past few years [7]. Szczuko et al [8] proposed a data fusion-based approach to multi-modal biometrics for the verification of bank clients.…”

Section: Background and Related Workmentioning

confidence: 99%

“…The Dempster-Shafer method was used for data fusion, and the outcome was acceptably accurate for banking applications. Almabdy and Elrefaei [7] reviewed recent advances in deep learning systems for applications of biometrics. Several performance indicators have shown that deep neural networks can provide the high accuracy required for verifying human identity.…”

Section: Background and Related Workmentioning

confidence: 99%

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Cyber-Physical Customer Management for Internet of Robotic Things-Enabled Banking

2023

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In-person banking is still an important part of financial services around the world. Hybrid bank branches with service robots can improve efficiency and reduce operating costs. An efficient autonomous Know-Your-Customer (KYC) is required for hybrid banking. In this paper, an automated deep learning-based framework for interbank KYC in robot-based cyber-physical banking is proposed. A deep biometric architecture was used to model the customer's KYC and anonymise the collected visual data to ensure the customer's privacy. The symmetric-asymmetric encryption-decryption module in addition to the blockchain network was used for secure and decentralized transmission and validation of the biometric information. A high-capacity fragile watermarking algorithm based on the integer-to-integer discrete wavelet transform in combination with the Z6 and A6 lattice vector quantization for the secure transmission and storage of inperson banking documents is also proposed. The proposed framework was simulated and validated using a Pepper humanoid robot for the automated biometric-based collection of handwritten bank checks from customers adhering to COVID-19 pandemic safety guidelines. The biometric information of bank customers such as fingerprint and name is embedded as a watermark in the related bank documents using the proposed framework. The results show that the proposed security protection framework can embed more biometric data in bank documents in comparison with similar algorithms. Furthermore, the quality of the secured bank documents is 20% higher in comparison with other proposed algorithms. Also, the hierarchal visual information communication and storage module that anonymizes the identity of people in videos collected by robots can satisfy the privacy requirements of the banks. Overall, the proposed framework can provide a rapid, efficient, and cost-effective inter-bank solution for future in-person banking while adhering to the security requirements and banking regulations.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Cyber-Physical Customer Management for Internet of Robotic Things-Enabled Banking

2023

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“…In his study, Almabdy [14] presents a comprehensive overview of deep learning techniques used in biometric systems, demonstrating their ability to handle various biometric modalities including fingerprints, faces, iris, and voice. The learning of intricate patterns and features from raw data has been demonstrated by deep learning methods such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs).…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Machine Learning Classifiers Performance with Feature Extraction for Face Recognition

Abusham

2023

Preprint

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It is crucial to select the right machine learning classifier for image classification and face recog-nition. This study examines the effectiveness of four different face recognition classifiers - Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), and Neural Networks. An analysis of the Large Faces in the Wild (LFW) dataset was carried out using Principal Component Analysis (PCA). Classifiers are rigorously trained and evaluated based on the extracted features. Comparison of classifier performance is an insightful way to figure out their strengths and weaknesses. Having a visual representation of the classifier's performance gives a complete understanding of its capabilities. Through the selection of the most appropriate classifier, study results contribute to advancements in image classification, recognition, and biometric identification. The comparison study demonstrated that the Neural Network classifier was exceptionally accurate and proficient in recognizing faces from the LFW dataset when used in conjunction with PCA for feature extraction. According to the comparative analysis, the Neural Network classifier proved exceptionally accurate and proficient at identifying faces from the LFW dataset when combined with PCA for feature extraction.

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“…The biometric system can be generally classified into 2 types, namely unimodal, and multimodal biometric systems [13]. The unimodal biometric structure will establish the person's identity related to a single information source, like face, left iris, and right iris [14]. While in multimodal biometric technique it functions under identification mode, result of the technique can be observed by a ranks list gained from candidate, which indicates likely matches.…”

Section: Introductionmentioning

confidence: 99%

Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification Model

Nair

Kumar³

2022

IJRITCC

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Biometric security has become a main concern in the data security field. Over the years, initiatives in the biometrics field had an increasing growth rate. The multimodal biometric method with greater recognition and precision rate for smart cities remains to be a challenge. By comparison, made with the single biometric recognition, we considered the multimodal biometric recognition related to finger vein and fingerprint since it has high security, accurate recognition, and convenient sample collection. This article presents a Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification (MFFODL-MBV) model. The presented MFFODL-MBV technique performs biometric verification using multiple biometrics such as fingerprint, DNA, and microarray. In the presented MFFODL-MBV technique, EfficientNet model is employed for feature extraction. For biometric recognition, MFFO algorithm with long short-term memory (LSTM) model is applied with MFFO algorithm as hyperparameter optimizer. To ensure the improved outcomes of the MFFODL-MBV approach, a widespread experimental analysis was performed. The wide-ranging experimental analysis reported improvements in the MFFODL-MBV technique over other models.

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An Overview of Deep Learning Techniques for Biometric Systems

Cited by 8 publications

References 58 publications

Cyber-Physical Customer Management for Internet of Robotic Things-Enabled Banking

Cyber-Physical Customer Management for Internet of Robotic Things-Enabled Banking

A Comparative Study of Machine Learning Classifiers Performance with Feature Extraction for Face Recognition

Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification Model

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