Multimodal Biometric Authentication Research Articles

Comparative Analysis of Firefly and Modified Firefly Algorithms in Multimodal Biometric Authentication Systems

Abstract: This research focuses on addressing the challenges of high-dimensional feature spaces and selecting significant features in multimodal biometric systems. Feature-level fusion poses persistent issues that require extensive investigation due to its potential for enhancing biometric recognition accuracy. This study proposes the integration of meta-heuristic optimization techniques into the feature selection phase of a multimodal biometric system, prior to the classification phase, to identify the most relevant features from two distinct biological traits. To determine the authorization status of an individual, the fused feature vectors are inputted into a Support Vector Machine (SVM) classifier. The study leverages the physiological biometrics of the face and iris to validate the findings of previous research, highlighting the exceptional accuracy of iris recognition and the natural acceptability of face recognition for identity verification purposes.

Face-voice based multimodal biometric authentication system via FaceNet and GMM.

Information security has become an inseparable aspect of the field of information technology as a result of advancements in the industry. Authentication is crucial when it comes to dealing with security. A user must be identified using biometrics based on certain physiological and behavioral markers. To validate or establish the identification of an individual requesting their services, a variety of systems require trustworthy personal recognition schemes. The goal of such systems is to ensure that the offered services are only accessible by authorized users and not by others. This case study provides enhanced accuracy for multimodal biometric authentication based on voice and face hence, reducing the equal error rate. The proposed scheme utilizes the Gaussian mixture model for voice recognition, FaceNet model for face recognition and score level fusion to determine the identity of the user. The results reveal that the proposed scheme has the lowest equal error rate in comparison to the previous work.

Multimodal biometric recognition using fused handcrafted features

AbstractMultimodal biometric systems have been widely implemented in a variety of real-world scenarios due to their ability to overcome limitations associated with unimodal biometric systems. This paper is focused on the combination of the face, ear and gait in a unified multimodal biometric identification system using handcrafted features. These approaches provide robust and discriminative features to solve the biometric problem. In this research, speed up robust features and histogram of oriented gradients approaches have been used to extract features from face, ear and gait. The extracted features are optimized using genetic algorithm and classified using Levenberg-Marquardt backpropagation neural network. The system performance is evaluated on constrained and unconstrained dataset conditions.

Multimodal biometric authentication method by federated learning

The Internet of Health Things requires rigid security policies to control access to sensitive data. However, nowadays, classic methods for user authentication may not meet the requirements for protection against unauthorized users during the collection, storage, and transmission of data. Therefore, there is a need for the evolution of technologies that allows the authentication of users based on unique personal identifiers (biometric characteristics). This work presents a security management approach for authentication that stands out for using two combined convolutional neural networks (CNN) for the biometric identification of users. The new approach relies on Federated Learning (FL) which is a Machine Learning paradigm that can support data management and privacy by training decentralized models collaboratively without effective data exchange. The new approach also combines Photoplethysmography and Electrocardiogram signals which improves identification accuracy and establishes a multimodal authentication. In sum, the new security management approach achieves high Accuracy, and a low false acceptance rate, guaranteeing protection against unauthorized access attempts.

English

A multi modality biometric authentication system can combine information from various modalities and provides accurate results compared to biometric systems used individually. A novel ensemble classifier-based multimodal biometric authentication system has been proposed in this work. The performance of the proposed multimodal authentication system is measured using parameters such as Accuracy, Sensitivity and Specificity and compared with the SVM classifier, Decision tree classifier when fingerprint, Iris, and Face features are used. The results of the multimodal biometric system are also compared with the biometric authentication system when fingerprint features are used and combined with Fingerprint & Iris features. The proposed ensemble classifier-based multimodal biometric authentication system provides an accuracy of 96.75%, Sensitivity of 94.74%, Specificity of 98.95%, FAR of 1.04 and FRR of 5.26. The proposed ensemble classifier outperforms SVM and decision tree classifiers regarding performance measures.

Retraction Note: Multi-modal biometric authentication system based on face and signature using legion feature estimation technique

Retraction Note: Multi-modal biometric authentication system based on face and signature using legion feature estimation technique

APPLICATIONS OF MULTIMODAL BIOMETRICS AUTHENTICATION FOR ENHANCING THE IOT SECURITY USING DEEP LEARNING

The Internet of Things (IoT) integrates billions of electronic devices into computer networks to provide advanced and intelligent services that enable devices to communicate with each other by exchanging information with minimal human interaction. The security issue is at higher risk in IoT systems than in other computing systems. Maintaining the security requirement when attacking the physical surface of the IoT system device is a challenging task. Implementing security mechanisms like authentication and access control for the IoT ecosystem is necessarily needed to ensure the security of IoT devices. The key used for security may be stolen, forgotten, or forged. Also, the key may be generated by intruders or men in the middle of traditional security mechanisms. Biometric security is becoming more advanced and sophisticated with technological advancements and is mostly used in authentication systems. In unimodal biometrics, only one biometrics character can be applied which does not apply to ensure the security of IoT systems. In this paper, Multimodal biometrics authentication was used for securing edge devices in the IoT ecosystems. Face image and fingerprint image were used as multimodal biometrics systems for authenticating users to secured IoT devices. A pi-camera module and fingerprint sensor were used to capture biometric data. Image processing techniques were then applied to the images. Then CNN algorithms were used for feature extraction and model creation. During model creation, the RELU function was used as an activation function, soft-max for image classification, and Max-pooling for image dimensional reduction which helped the model speed up the training process. Experimental results show that the accuracy of the face image and fingerprint image is 92% and 89% respectively, which is a promising result that achieves the objective of the study.
 Keywords: Internet of Things, Multimodal Biometrics, Authentication, CNN, Deep Learning

Multimodal biometrics authentication using extreme learning machine with feature reduction by adaptive particle swarm optimization

Multimodal biometrics authentication using extreme learning machine with feature reduction by adaptive particle swarm optimization

Multimodal Biometrics Authentication in Healthcare Using Improved Convolution Deep Learning Model

In healthcare applications, biometric authentication is crucial in managing patient credential details. The limited usage of biometric traits causes personal details theft, treatment hacking, and payment hijacking. Multimodal biometrics should incorporate into the healthcare system to maintain security and privacy in healthcare applications. Previous methods ensure authentication and security consume high computation complexity and fail to maintain system reliability and scalability. Therefore, in this work, an improved convolution deep learning model (ICDLM) is applied to manage the patient traits in the healthcare center. Initially, Experimentation is conducted with SWAN DATASET, which consists of face, voice, and periocular biometric features. The biometric characteristics are investigated according to the super point convolution neural model. The model analyzes the trait descriptors and interest points to identify the patterns. The extracted patterns are utilized to match the patient while accessing the information in healthcare applications. This process uses template pattern matching (TPM), which minimizes the absolute difference while authenticating the users. The system’s efficiency implemented using Python and excellency is compared with traditional approaches. The system attains 98.31% and 98.67% accuracy on the number of users and attempts with a minimum false identification rate of 0.0125 and 0.0128 for several users and attempts.

FIMBISAE: A Multimodal Biometric Secured Data Access Framework for Internet of Medical Things Ecosystem

Information from the Internet of Medical Things (IoMT) domain demands building safeguards against illegitimate access and identification. Existing user identification schemes suffer from challenges in detecting impersonation attacks which leave systems vulnerable and susceptible to misuse. Significant advancement has been achieved in the domain of biometrics and health informatics. This can take a step ahead with the usage of multimodal biometrics for the identification of healthcare system users. With this aim, the proposed work explores the fingerprint and iris modality to develop a multimodal biometric data identification and access control system for the healthcare ecosystem. In the proposed approach, minutiae-based fingerprint features and a combination of local and global iris features are considered for identification. Further, an index space based on the dimension of the feature vector is created, which gives a 1-D embedding of the high-dimensional feature set. Next, to minimize the impact of false rejection, the approach considers the possible deviation in each element of the feature vector and then stores the data in possible locations using the predefined threshold. Besides, to reduce the false acceptance rate, linking of the modalities has been done for every individual data. The modality linking thus helps in carrying out an efficient search of the queried data, thereby minimizing the false acceptance and rejection rate. Experiments on a chimeric iris and fingerprint bimodal database resulted in an average of 95% reduction in the search space at a hit rate of 98%. The results suggest that the proposed indexing scheme has the potential to substantially reduce the response time without compromising the accuracy of identification.

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

Identity management describes a problem by providing the authorized owners with safe and simple access to information and solutions for specific identification processes. The shortcomings of the unimodal systems have been addressed by the introduction of multimodal biometric systems. The use of multimodal systems has increased the biometric system’s overall recognition rate. A new degree of fusion, known as an intelligent Dual Multimodal Biometric Authentication Scheme, is established in this study. In the proposed work, two multimodal biometric systems are developed by combining three unimodal biometric systems. ECG, sclera, and fingerprint are the unimodal systems selected for this work. The sequential model biometric system is developed using a decision-level fusion based on WOA-ANN. The parallel model biometric system is developed using a score-level fusion based on SSA-DBN. The biometric authentication performs preprocessing, feature extraction, matching, and scoring for each unimodal system. On each biometric attribute, matching scores and individual accuracy are cyphered independently. A matcher performance-based fusion procedure is demonstrated for the three biometric qualities because the matchers on these three traits produce varying values. The two-level fusion technique (score and feature) is implemented separately, and their results with the current scheme are compared to exhibit the optimum model. The suggested plan makes use of the highest TPR, FPR, and accuracy rates.

Secure personal authentication in fog devices via multimodal rank‐level fusion

SummaryIn recent years, Fog and edge computing have gained enormous increases and usage of Internet of Things (IoT) devices in research and development even though it provides challenges such as security and privacy. One way to ensure this is a fusion of multimodal biometrics authentication systems, which offer highly reliable biometric authentication solutions to reduce the failure to enroll rate, increase the degree of freedom, and deter spoof attacks. Since unimodal biometric systems often face significant limitations due to sensitivity to noise, data quality, non‐universality, and illumination variations. In this research, we propose management of access control to ensure the desired level of security, a Rank level fusion integration method. ORL, GT, CASIA, and VGG‐face databases were used for testing and evaluating the implemented system. Thus, using the LDA with SCE based feature selection approach, the average GAR percent for the facial, hearing, and palm vein is 3.25% for the facial and muffs pulse and 8.55% for the head and palm vein result was the overall performance of the biometric system even in the presence of low‐quality data in the Fog environment.

Preserving personal health records security and privacy using C-R3D algorithm and multimodal biometric authentication

Preserving personal health records security and privacy using C-R3D algorithm and multimodal biometric authentication

Preserving personal health records security and privacy using C-R3D algorithm and multimodal biometric authentication

Preserving personal health records security and privacy using C-R3D algorithm and multimodal biometric authentication

Fused Multimodal Biometric Authentication for an Open Distance Learning Environment Online Examination

Fused Multimodal Biometric Authentication for an Open Distance Learning Environment Online Examination

SIPFormer: Segmentation of Multiocular Biometric Traits With Transformers

The advancements in machine vision have opened up new avenues for implementing multimodal biometric identification systems for real-world applications. These systems can address the shortcomings of unimodal biometric systems, which are susceptible to spoofing, noise, nonuniversality, and intraclass variations. Besides, ocular traits among various biometric traits are preferably used in these recognition systems due to their great uniqueness, permanence, and performance. However, segmenting visual biometric features under unconstrained situations remains challenging due to a variety of variables, such as Purkinje reflexes, specular reflections, eye gaze, off-angle pictures, poor resolution, and numerous occlusions. To overcome these challenges, this research presents a novel framework called SIPFormer, comprising the encoder, decoder, and transformer blocks to simultaneously segment three ocular traits (sclera, iris, and pupil) using its discriminative multihead self-attention mechanism. Besides, we used the large publicly available iris database reflecting different unconstrained acquisition settings, with inherent noise effects such as scanner artifacts, intensity and illumination variations, motion blur, and occultations caused by eyelashes, eyelids, and eyeglasses. Furthermore, the simulation results demonstrate the efficacy of the proposed SIPFormer model, where it achieved the mean Dice similarity coefficient scores of 0.9018, 0.9176, and 0.9229 for segmenting the sclera, iris, and pupil classes, respectively.

Multimodal Biometric Watermarking-based Transfer Learning Authentication

Multimodal Biometric Watermarking-based Transfer Learning Authentication

A Novel Multimodal Biometric Authentication Framework Using Rule-Based ANFIS Based on Hybrid Level Fusion

A Novel Multimodal Biometric Authentication Framework Using Rule-Based ANFIS Based on Hybrid Level Fusion

The effects of anthropomorphism and multimodal biometric authentication on the user experience of voice intelligence.

Voice intelligence is a revolutionary “zero-touch” type of human-machine interaction based on spoken language. There has been a recent increase in the number and variations of voice assistants and applications that help users to acquire information. The increased popularity of voice intelligence, however, has not been reflected in the customer value chain. Current research on the socio-technological aspects of human-technology interaction has emphasized the importance of anthropomorphism and user identification in the adoption of the technology. Prior research has also pointed out that user perception toward the technology is key to its adoption. Therefore, this research examines how anthropomorphism and multimodal biometric authentication influence the adoption of voice intelligence through user perception in the customer value chain. In this study we conducted a between-subjects online experiment. We designed a 2 × 2 factorial experiment by manipulating anthropomorphism and multimodal biometric authentication into four conditions, namely with and without a combination of these two factors. Subjects were recruited from Amazon MTurk platform and randomly assigned to one of the four conditions. The results drawn from the empirical study showed a significant direct positive effect of anthropomorphism and multimodal biometric authentication on user adoption of voice intelligence in the customer value chain. Moreover, the effect of anthropomorphism is partially mediated by users' perceived ease of use, perceived usefulness, and perceived security risk. This research contributes to the existing literature on human-computer interaction and voice intelligence by empirically testing the simultaneous impact of anthropomorphism and biometric authentication on users' experience of the technology. The study also provides practitioners who wish to adopt voice intelligence in the commercial environment with insights into the user interface design.

An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services

With the advent of modern technologies, the healthcare industry is moving towards a more personalised smart care model. The enablers of such care models are the Internet of Things (IoT) and Artificial Intelligence (AI). These technologies collect and analyse data from persons in care to alert relevant parties if any anomaly is detected in a patient’s regular pattern. However, such reliance on IoT devices to capture continuous data extends the attack surfaces and demands high-security measures. Both patients and devices need to be authenticated to mitigate a large number of attack vectors. The biometric authentication method has been seen as a promising technique in these scenarios. To this end, this paper proposes an AI-based multimodal biometric authentication model for single and group-based users’ device-level authentication that increases protection against the traditional single modal approach. To test the efficacy of the proposed model, a series of AI models are trained and tested using physiological biometric features such as ECG (Electrocardiogram) and PPG (Photoplethysmography) signals from five public datasets available in Physionet and Mendeley data repositories. The multimodal fusion authentication model shows promising results with 99.8% accuracy and an Equal Error Rate (EER) of 0.16.