Open AccessConvolutional Neural Network Based Multimodal Biometric Human Authentication using Face, Palm Veins and Fingerprint
Author(s) -
Priti Shende*,
Yogesh H. Dandwate
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.c8467.019320
Subject(s) - biometrics , computer science , convolutional neural network , artificial intelligence , pattern recognition (psychology) , word error rate , palm print , classifier (uml) , robustness (evolution) , support vector machine , fingerprint (computing) , facial recognition system , computer vision , machine learning , biochemistry , chemistry , gene
security access control systems and forensic applications. Performance of conventional unimodal biometric systems is generally suffered due to the noisy data, non universality and intolerable error rate. In propose system, multi layer Convolutional Neural Network (CNN) is applied to multimodal biometric human authentication using face, palm vein and fingerprints to increase the robustness of system. For the classification linear Support Vector Machine classifier is used. For the evaluation of system self developed face, palm vein and fingerprint database having 4,500 images are used. The performance of the system is evaluated on the basis of % recognition accuracy, and it shows significant improvement over the unimodal-biometric system and existing multimodal systems.