Abstract
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.
Highlights
Biometric recognition has become an important part of daily life applications such as forensics, surveillance systems, attendance systems, unlocking the smart phones, Automated Tailored Machines (ATMs) and border and control systems in many countries due to the extensive research in this field [1]
In contrast to passwords and Personal Identification Numbers (PINs), the human biometrics is so naturally associated with the owner that it cannot be changed
Deep learning models based on the Convolutional Neural Network (CNN) architecture have been proposed for the feature level fusion of online signatures and fingerprints
Summary
Biometric recognition has become an important part of daily life applications such as forensics, surveillance systems, attendance systems, unlocking the smart phones, Automated Tailored Machines (ATMs) and border and control systems in many countries due to the extensive research in this field [1]. The data were obtained by security researchers just through the web browser by applying some operations This shows that the biometric data of users can be compromised, it should be stored in such a fused and encrypted form, so that it cannot be reused, even if compromised. As there are only a few databases that contain samples from both fingerprint and online signature biometric traits, there was a strong need to collect the data on a large-scale from the real users. For this reason, the data are collected from 280 subjects and utilized in this research.
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