Feature-level fusion of fingerprint and online signature for multimodal biometrics

Leghari, Mehwish; Memon, Saima Q.; Chandio, Asghar Ali

doi:10.1109/icomet.2018.8346358

Cited by 9 publications

(5 citation statements)

References 15 publications

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“…The main goal of [26] novel strategy is to protect fingerprint and signature data against theft and infiltration by fusing at the feature level, which eliminates the possibility of faked data. [27] fused biometric information at the feature level from a person's face, iris, and signature modalities.…”

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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“…To the best of our knowledge there has been no previous research on feature level fusion of fingerprints and online signatures using deep learning techniques. The feature level fusion of fingerprint and online signature was proposed in [32] however, they only proposed the fusion scheme and no experimental results were presented. Furthermore, the fingerprints and online signatures were fused at feature level using machine learning approach by Imran et al [33].…”

Section: Related Workmentioning

confidence: 99%

Deep Feature Fusion of Fingerprint and Online Signature for Multimodal Biometrics

et al. 2021

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The extensive research in the field of multimodal biometrics by the research community and the advent of modern technology has compelled the use of multimodal biometrics in real life applications. Biometric systems that are based on a single modality have many constraints like noise, less universality, intra class variations and spoof attacks. On the other hand, multimodal biometric systems are gaining greater attention because of their high accuracy, increased reliability and enhanced security. This research paper proposes and develops a Convolutional Neural Network (CNN) based model for the feature level fusion of fingerprint and online signature. Two types of feature level fusion schemes for the fingerprint and online signature have been implemented in this paper. The first scheme named early fusion combines the features of fingerprints and online signatures before the fully connected layers, while the second fusion scheme named late fusion combines the features after fully connected layers. To train and test the proposed model, a new multimodal dataset consisting of 1400 samples of fingerprints and 1400 samples of online signatures from 280 subjects was collected. To train the proposed model more effectively, the size of the training data was further increased using augmentation techniques. The experimental results show an accuracy of 99.10% achieved with early feature fusion scheme, while 98.35% was achieved with late feature fusion scheme.

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“…This can be seen over many research projects spanning the last fifteen plus years [8] [9]. The current application of liveness differs dramatically depending on the intra-fusion requirements and can be seen in current literature [10] [11] [12]. It is an expected factor of biometric security that liveness needs to be included if security is the main requirement of the installation; otherwise, there runs the risk of compromising the system.…”

Section: Coercion and Liveness Detectionmentioning

confidence: 99%

An algorithmic approach for optimising biometric systems using liveness and coercion detection

Matthew

Canning

2020

Computers & Security

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This paper looks at the possibility of creating an algorithm that will combine liveness and coercion modalities, along with organisational factors such as workforce composition. The hypothesis is that the algorithm can produce a value that can further be used to compare different setups of biometric security for self-optimisation by taking in the context of technique compatibility and user requirements. To this end, the algorithm focuses on four main aspects: time, participants, anomalous user-bases, and device redundancy inside a typical organisation. An experimental methodology has been used, focusing on the development of the algorithm, its associated effects, and how different parameters can be reliably estimated. After testing, the algorithm is proved to work as it creates an appropriate value, called the security value, which can be used to discover the best combinations of modalities for fusion development or practical installation for a given situation. There are some issues with this primarily due to data provision, the requirements for more data to parse through the algorithm, and finally, the need for a suitable interface, otherwise it may be too complex for efficient usage in a traditional security environment. There are potential implications within a general security application such as liveness and coercion multimodal fusion and autonomous system development and pervasive environments, allowing dynamic security systems to be developed. However, the main focus of this algorithm is to highlight the fusion of liveness and coercion detection and how they can be best applied to specific security scenarios.

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Feature-level fusion of fingerprint and online signature for multimodal biometrics

Cited by 9 publications

References 15 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

Deep Feature Fusion of Fingerprint and Online Signature for Multimodal Biometrics

An algorithmic approach for optimising biometric systems using liveness and coercion detection

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