Image fusion by discrete wavelet transform for multimodal biometric recognition

Channegowda, Arjun Benagatte; Prakash, H. N.

doi:10.11591/ijai.v11.i1.pp229-237

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…The proposed system's performance was measured in terms of SVM classifier average accuracy and reaction time which demonstrated that feature level fusion using CCA and an SVM classifier improves classification accuracy while requiring less training and testing time. Another study [24] built a reliable multimodal identification system using feature-level fusion. For 40 people from the ORL and CASIA-V1 databases, the system's recognition accuracy was improved by combining face and iris features.…”

Section: Literature Reviewmentioning

confidence: 99%

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

T. A.

2023

IJRASET

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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.

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Section: Literature Reviewmentioning

confidence: 99%

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

T. A.

2023

IJRASET

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“…The efficiency of each biometric system is nonetheless constrained by the intrinsic characteristics of biometric traits and the limits of detecting technology. Multimodal biometric fusion has therefore lately caught the interest of several academics [5], [6]. Combining two or more biometric qualities from several people is an efficient way to overcome some of the limitations of using a single biometric system.…”

Section: Introductionmentioning

confidence: 99%

A multimodal biometric database and case study for face recognition based deep learning

Kadhim,

Hasan Abdulameer

2024

Bulletin EEI

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Recently, multimodal biometric systems have garnered a lot of interest for the identification of human identity. The accessibility of the database is one of the contributing elements that impact biometric recognition systems. In their studies, the majority of researchers concentrate on unimodal databases. There was a need to compile a fresh, realistic multimodal biometric database, nonetheless, because there were so few comparable multimodal biometric databases that were publically accessible. This study introduces the MULBv1 multimodal biometric database, which contains homologous biometric traits. The MULBv1 database includes 20 images of each person's face in various poses, facial emotions, and accessories, 20 images of their right hand from various angles, and 20 images of their right iris from various lighting positions. The database contains real multimodal data from 174 people, and all biometrics were accurately collected using the micro camera of the iPhone 14 Pro Max. A face recognition technique is also suggested as a case study using the gathered facial features. In the case study, the deep convolutional neural network (CNN) was used, and the findings were positive. Through several trials, the accuracy was (97.41%).

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“…In this study, the physiological biometrics of the face and iris are used to support the finding of [26] that iris recognition is one of the most accurate biometrics while face recognition is the most natural and acceptable for use in identity verification.…”

Section: Introductionmentioning

confidence: 99%

A framework for Modified Firefly Algorithm in Multimodal Biometric Authentication System

Bukola

2023

int. jour. eng. com. sci

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Many end users are turning to multimodal biometric systems as a result of the limitations of conventional authentication techniques and unimodal biometric systems for offering a high level of accurate authentication. When high accuracy and security are required, multimodal biometrics are the best choice because to the utilization of numerous identification modalities. It is difficult to identify the best features that contribute to the recognition rate/accuracy and have a high redundancy of features since different features are acquired at the feature level fusion from a variety of physiological or behavioral variables. At the feature selection level, the utilization of meta-heuristic algorithms will reduce the number of redundant features while keeping critical feature sets that are important to biometric performance, accuracy, and efficiency. The study demonstrated a multimodal biometric authentication system that used the features of the face and both irises. In order to avoid being stuck at the local optimum and hasten convergence, the Firefly Algorithm (FFA) was modified by including a chaotic sinusoidal map function and a roulette wheel selection mechanism as deterministic processes. The results of the study demonstrated that in terms of sensitivity, precision, recognition accuracy, and time, the proposed MFFA with multimodal outperformed the MFFA for unimodal, bi-modal, and bi-instance. In addition to being computationally faster, more accurate, and suitable for real-time applications, the modified method, known as MFFA, proved effective in integrating multimodal data sets.

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Image fusion by discrete wavelet transform for multimodal biometric recognition

Cited by 6 publications

References 16 publications

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

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

A multimodal biometric database and case study for face recognition based deep learning

A framework for Modified Firefly Algorithm in Multimodal Biometric Authentication System

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