Multimodal biometrics of fingerprint and signature recognition using multi-level feature fusion and deep learning techniques

Channegowda, Arjun Benagatte; Prakash, H. N.

doi:10.11591/ijeecs.v22.i1.pp187-195

Cited by 9 publications

(6 citation statements)

References 18 publications

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“…The result of the proposed method is markedly higher than that of [37], which uses score-level fusion to fuse (iris, face, finger vein, and palm print). Another study [38] employed multilevel feature fusion for signature and fingerprint biometrics. Both of these studies used a CNN algorithm for identification, which is a complex artificial intelligence algorithm for implementation.…”

Section: -Results Discussionmentioning

confidence: 99%

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Optimization Techniques for Human Multi-Biometric Recognition System

Yaseen Abdullah

2023

J. Al-Qadisiyah Comp. Sci. Math.

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Researchers are increasingly using multimodal biometrics to strengthen the security of biometric applications. In this study, a strong multimodal human identification model was developed to address the growing problem of spoofing attacks in biometric security systems. Through the use of metaheuristic optimization methods, such as the Genetic Algorithm(GA), Ant Colony Optimization(ACO), and Particle Swarm Optimization (PSO) for feature selection, this unique model incorporates three biometric modalities: face, iris, and fingerprint. Image pre-processing, feature extraction, critical image feature selection, and multibiometric recognition are the four main steps in the workflow of the system. To determine its performance, the model was evaluated on the SDUMLA-HMT dataset, which contains a variety of biometric features from various individuals. The system outperformed existing techniques in the literature with an excellent recognition accuracy of 99.4%. Although this result is encouraging, further research on larger and more varied datasets is necessary to confirm its applicability across many circumstances. This study highlights how multimodal biometrics strengthened by metaheuristic algorithms can considerably increase biometric security against spoofing assaults, thereby opening a promising new direction for future development in the field.

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Section: -Results Discussionmentioning

confidence: 99%

“…Feature-level fusion joins different characterizations to generate a single representation of a given individual. Consequently, a single feature vector was fed to the classifier algorithm [38].…”

Section: Feature Fusionmentioning

confidence: 99%

Optimization Techniques for Human Multi-Biometric Recognition System

Yaseen Abdullah

2023

J. Al-Qadisiyah Comp. Sci. Math.

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“…Separate unimodal experiments are conducted on face and signature using uniform local binary pattern (ULBP) features and histogram of oriented gradient (HOG) features [21]- [26]. In the entire case, an ensemble machine learning classifier is used [26].…”

Section: Face and Signature Fusion Experimentsmentioning

confidence: 99%

Image fusion by discrete wavelet transform for multimodal biometric recognition

Channegowda

Prakash

2022

IJ-AI

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In today’s world, security plays a crucial role in almost all applications. Providing security to a huge population is a more challenging task. Biometric security is the key player in such type of situation. Using a biometric-based security system more secure application can be built because it is tough to steal or forge. The unimodal biometric system uses only one biometric modality where some of the limitations will arise. For example, if we use fingerprints due to oiliness or scratches, the finger recognition rate may reduce. In order to overcome the drawbacks of unimodal biometrics, multimodal biometric systems were introduced. In this paper, new multimodal fusion methods are proposed, where instead of merging features, database images are fused using discrete wavelet transform (DWT) technique. Face and signature images are fused, features are extracted from the fused image, an ensemble classifier is used for classification, and also experiments are conducted for finger vein and signature images.

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“…This might improve overall matching accuracy and strengthen the security of biometric systems. There are several ways to research biometric fusion, one of which makes use of heterogeneous datasets [7], [8], that integrating biometric characteristics (such a fingerprint from a separate database and a signature from another. In the experiment, biometric characteristics from many people are combined to produce a "chimeric user."…”

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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Multimodal biometrics of fingerprint and signature recognition using multi-level feature fusion and deep learning techniques

Cited by 9 publications

References 18 publications

Optimization Techniques for Human Multi-Biometric Recognition System

Optimization Techniques for Human Multi-Biometric Recognition System

Image fusion by discrete wavelet transform for multimodal biometric recognition

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

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