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Open AccessArticle

Deep Feature Fusion of Fingerprint and Online Signature for Multimodal Biometrics

1
Institute of Information and Communication Technology, University of Sindh, Jamshoro 76080, Pakistan
2
Information Technology Department, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah 67480, Pakistan
*
Author to whom correspondence should be addressed.
Academic Editor: M. Ali Akber Dewan
Received: 9 December 2020 / Revised: 1 February 2021 / Accepted: 3 February 2021 / Published: 7 February 2021
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. View Full-Text
Keywords: biometric fusion; feature-level fusion; multimodal biometrics; fingerprint and online signature recognition; convolutional neural network biometric fusion; feature-level fusion; multimodal biometrics; fingerprint and online signature recognition; convolutional neural network
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MDPI and ACS Style

Leghari, M.; Memon, S.; Dhomeja, L.D.; Jalbani, A.H.; Chandio, A.A. Deep Feature Fusion of Fingerprint and Online Signature for Multimodal Biometrics. Computers 2021, 10, 21. https://0-doi-org.brum.beds.ac.uk/10.3390/computers10020021

AMA Style

Leghari M, Memon S, Dhomeja LD, Jalbani AH, Chandio AA. Deep Feature Fusion of Fingerprint and Online Signature for Multimodal Biometrics. Computers. 2021; 10(2):21. https://0-doi-org.brum.beds.ac.uk/10.3390/computers10020021

Chicago/Turabian Style

Leghari, Mehwish; Memon, Shahzad; Dhomeja, Lachhman D.; Jalbani, Akhtar H.; Chandio, Asghar A. 2021. "Deep Feature Fusion of Fingerprint and Online Signature for Multimodal Biometrics" Computers 10, no. 2: 21. https://0-doi-org.brum.beds.ac.uk/10.3390/computers10020021

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