FUSION OF MULTIMODAL BIOMETRICS OF FINGERPRINT, IRIS AND HAND WRITTEN SIGNATURES TRAITS USING DEEP LEARNING TECHNIQUE - YADAV, ASHOK KUMAR; SRINIVASULU, PROF. T. (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
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.