FUSION OF MULTIMODAL BIOMETRICS OF FINGERPRINT, IRIS AND HAND WRITTEN SIGNATURES TRAITS USING DEEP LEARNING TECHNIQUE

• Published in: Turkish journal of computer and mathematics education 2021-05, Vol.12 (11), p.1627-1638
• Main Authors: YADAV, ASHOK KUMAR, SRINIVASULU, PROF. T.
• Format: Article
• Language: English
• ISSN: 1309-4653; 1309-4653
• DOI: 10.17762/turcomat.v12i11.6098

Due to expanding interest for the data security and safety guidelines everywhere, biometricauthentication 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 aovercome the drawbacks of uni-model biometric frameworks. In Present research, novel multi biometricsrecognition proof solution is developed, that depends to deep learning techniques for perceiving human utilizingmulti biometric traits of Iris pattern, finger print data and offline signature biometrics. Framework of designdepends on Deep Neural Networks (DNNs), for separating the parameters & classification of the image utilizingsoft max based technique. To foster the framework, deep learning models are joined iris, finger print and off-linesignature. To construct the VGG-19 network was utilized, and Adam streamlining technique has been applied forunmitigated to measure the degree of inequality was utilized as a misfortune work. A few strategies to stay awayfrom overfitting were applied, like picture increase and drop-out procedures. For combining the deep learningnetworks, different combinations are utilized to investigate the impact of techniques on acknowledgmentexecution, accordingly component and score-level combination approach was applied. The exhibition of proposedframework is experimentally by directing a few trials to the SDUMLA-HMT data set, which is multi-modalbiometric data set. Acquired outcomes showed that involving triple biometrics attributes in biometric distinguishedproof frameworks got preferred outcomes over utilizing a couple biometric characteristics. The outcomesadditionally shows that our methodology serenely beat other condition of- - the-craftsmanship techniques byaccomplishing a precision of 99.11% on an element degree combination procedures and of 99.21 percentaccuracy of various strategy for fusion at score level.