FUSION OF MULTIMODAL BIOMETRICS OF FINGERPRINT, IRIS AND HAND WRITTEN SIGNATURES TRAITS USING DEEP LEARNING TECHNIQUE
Main Article Content
Abstract
Due to expanding interest for the data security and safety guidelines everywhere, biometric
authentication technology has been generally utilized in our regular day to day existence. With respects to this,
multi-modal biometric innovation has acquired attention and became famous because of the capacity to a
overcome the drawbacks of uni-model biometric frameworks. In Present research, novel multi biometrics
recognition proof solution is developed, that depends to deep learning techniques for perceiving human utilizing
multi biometric traits of Iris pattern, finger print data and offline signature biometrics. Framework of design
depends on Deep Neural Networks (DNNs), for separating the parameters & classification of the image utilizing
soft max based technique. To foster the framework, deep learning models are joined iris, finger print and off-line
signature. To construct the VGG-19 network was utilized, and Adam streamlining technique has been applied for
unmitigated to measure the degree of inequality was utilized as a misfortune work. A few strategies to stay away
from overfitting were applied, like picture increase and drop-out procedures. For combining the deep learning
networks, different combinations are utilized to investigate the impact of techniques on acknowledgment
execution, accordingly component and score-level combination approach was applied. The exhibition of proposed
framework is experimentally by directing a few trials to the SDUMLA-HMT data set, which is multi-modal
biometric data set. Acquired outcomes showed that involving triple biometrics attributes in biometric distinguished
proof frameworks got preferred outcomes over utilizing a couple biometric characteristics. The outcomes
additionally shows that our methodology serenely beat other condition of- - the-craftsmanship techniques by
accomplishing a precision of 99.11% on an element degree combination procedures and of 99.21 percent
accuracy of various strategy for fusion at score level.
Downloads
Metrics
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
