A Dual Multimodal Biometric Authentication System Based on WOA-ANN and SSA-DBN Techniques

Singh, Sandeep; Tiwari, Shamik

doi:10.3390/sci5010010

Cited by 9 publications

(5 citation statements)

References 16 publications

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“…Singh and Tiwari [20] integrated ECG, sclera, and fingerprint into two multimodal biometric frameworks using flexible and score-level combinations. Pre-processing steps, including extraction, sorting, and scoring, were applied to each unimodal construction.…”

Section: Related Workmentioning

confidence: 99%

“…The interval provides a measure of certainty and helps evaluate the model's reliability, inform decision thresholds, assess risks, monitor performance, and optimize the authentication system for enhanced access control. The equation for calculating the 90% confidence interval in the context of evaluating hybrid model architecture for a biometric authentication system is as (20):…”

Section: System Optimizationmentioning

confidence: 99%

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Optimizing dual modal biometric authentication: hybrid HPO-ANFIS and HPO-CNN framework

Singh,

Tiwari

2024

IJEECS

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In the realm of secure data access, biometric authentication frameworks are vital. This work proposes a hybrid model, with a 90% confidence interval, that combines "hyperparameter optimization-adaptive neuro-fuzzy inference system (HPO-ANFIS)" parallel and "hyperparameter optimization-convolutional neural network (HPO-CNN)" sequential techniques. This approach addresses challenges in feature selection, hyperparameter optimization (HPO), and classification in dual multimodal biometric authentication. HPO-ANFIS optimizes feature selection, enhancing discriminative abilities, resulting in improved accuracy and reduced false acceptance and rejection rates in the parallel modal architecture. Meanwhile, HPO-CNN focuses on optimizing network designs and parameters in the sequential modal architecture. The hybrid model's 90% confidence interval ensures accurate and statistically significant performance evaluation, enhancing overall system accuracy, precision, recall, F1 score, and specificity. Through rigorous analysis and comparison, the hybrid model surpasses existing approaches across critical criteria, providing an advanced solution for secure and accurate biometric authentication.

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Section: Related Workmentioning

confidence: 99%

Section: System Optimizationmentioning

confidence: 99%

Optimizing dual modal biometric authentication: hybrid HPO-ANFIS and HPO-CNN framework

Singh,

Tiwari

2024

IJEECS

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“…The two factors were concatenated in a feature-level manner. Sing and Tiwari [40] proposed biometric authentication using a CNN structure called the parallel Sparrow Search Algorithm-Deep Belief Network (SSA-DBN), consisting of seven layers, in which the three factors are ECG signals, fingerprints, and sclera images. Multimodality was achieved through inputting signals and images simultaneously, and SSA-DBN classified data from 80 people with an accuracy of 97.13%.…”

Section: Related Workmentioning

confidence: 99%

“…Ahamed et al [38] and Ammour et al [39] suggested systems with 17 layers and 50 layers, respectively, which are not suitable for use in mobile devices. Sing and Tiwari [40] proposed a relatively lightweight system with seven layers, but did not consider the safety of the data. The system of Rajasekar et al [41] recognizes only the image as a factor and, so, it cannot implement multi-modality.…”

Section: Related Workmentioning

confidence: 99%

CNN-Based Multi-Factor Authentication System for Mobile Devices Using Faces and Passwords

Han

2024

Applied Sciences

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Multi-factor authentication (MFA) is a system for authenticating an individual’s identity using two or more pieces of data (known as factors). The reason for using more than two factors is to further strengthen security through the use of additional data for identity authentication. Sequential MFA requires a number of steps to be followed in sequence for authentication; for example, with three factors, the system requires three authentication steps. In this case, to proceed with MFA using a deep learning approach, three artificial neural networks (ANNs) are needed. In contrast, in parallel MFA, the authentication steps are processed simultaneously. This means that processing is possible with only one ANN. A convolutional neural network (CNN) is a method for learning images through the use of convolutional layers, and researchers have proposed several systems for MFA using CNNs in which various modalities have been employed, such as images, handwritten text for authentication, and multi-image data for machine learning of facial emotion. This study proposes a CNN-based parallel MFA system that uses concatenation. The three factors used for learning are a face image, an image converted from a password, and a specific image designated by the user. In addition, a secure password image is created at different bit-positions, enabling the user to securely hide their password information. Furthermore, users designate a specific image other than their face as an auxiliary image, which could be a photo of their pet dog or favorite fruit, or an image of one of their possessions, such as a car. In this way, authentication is rendered possible through learning the three factors—that is, the face, password, and specific auxiliary image—using the CNN. The contribution that this study makes to the existing body of knowledge is demonstrating that the development of an MFA system using a lightweight, mobile, multi-factor CNN (MMCNN), which can even be used in mobile devices due to its low number of parameters, is possible. Furthermore, an algorithm that can securely transform a text password into an image is proposed, and it is demonstrated that the three considered factors have the same weight of information for authentication based on the false acceptance rate (FAR) values experimentally obtained with the proposed system.

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“…Singh and Tiwari. [21] combined ECG, sclera, and fingerprint to develop a multimodal biometric system. This combination is carried out in two modules: i) decision-level fusion where combined Whale Optimization (WOA-ANN) is used to generate the sequential model and ii) score-level fusion model which uses salp swarm optimization-deep belief network (SSA-DBN) model.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based biometric authentication using electrocardiogram and iris

Kailas,

Keshava Murthy

2024

IJ-AI

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Authentication systems play an important role in wide range of applications. The traditional token certificate and password-based authentication systems are now replaced by biometric authentication systems. Generally, these authentication systems are based on the data obtained from face, iris, electrocardiogram (ECG), fingerprint and palm print. But these types of models are unimodal authentication, which suffer from accuracy and reliability issues. In this regard, multimodal biometric authentication systems have gained huge attention to develop the robust authentication systems. Moreover, the current development in deep learning schemes have proliferated to develop more robust architecture to overcome the issues of tradition machine learning based authentication systems. In this work, we have adopted ECG and iris data and trained the obtained features with the help of hybrid convolutional neural network- long short-term memory (CNN-LSTM) model. In ECG, R peak detection is considered as an important aspect for feature extraction and morphological features are extracted. Similarly, gabor-wavelet, gray level co-occurrence matrix (GLCM), gray level difference matrix (GLDM) and principal component analysis (PCA) based feature extraction methods are applied on iris data. The final feature vector is obtained from MIT-BIH and IIT Delhi Iris dataset which is trained and tested by using CNN-LSTM. The experimental analysis shows that the proposed approach achieves average accuracy, precision, and F1-core as 0.985, 0.962 and 0.975, respectively.

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A Dual Multimodal Biometric Authentication System Based on WOA-ANN and SSA-DBN Techniques

Cited by 9 publications

References 16 publications

Optimizing dual modal biometric authentication: hybrid HPO-ANFIS and HPO-CNN framework

Optimizing dual modal biometric authentication: hybrid HPO-ANFIS and HPO-CNN framework

CNN-Based Multi-Factor Authentication System for Mobile Devices Using Faces and Passwords

Deep learning based biometric authentication using electrocardiogram and iris

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