Open AccessOptimized Multi-Model Biometric Based Human Authentication using Deep Neural Network
Author(s) -
Ravindra K. Ahuja,
Latika Duhan
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c1066.1183s319
Subject(s) - biometrics , computer science , artificial intelligence , artificial neural network , authentication (law) , pattern recognition (psychology) , process (computing) , feature extraction , local binary patterns , fingerprint (computing) , histogram equalization , feature (linguistics) , histogram , matlab , computer vision , image (mathematics) , linguistics , philosophy , computer security , operating system
Biometrics provides greater security and usability than conventional personal authentication methods. Fingerprints, facial identification systems and voice recognition systems are the features that biometric systems can use. To improve biometric authentication, the proposed method considered that the input image is iris and fingerprint; at first, pre-processing is performed through histogram equalization for all image inputs to enhance the image quality. Then the extraction process of the feature will be performed. The suggested method uses modified Local Binary Pattern (MLBP), GLCM with orientation transformation, and DWT features next to the extracted features to be combined for feature extraction. Then the optimum function is found with the Rider Optimization Algorithm (ROA) for all MLBP, GLCM and DWT. Eventually, the approach suggested is accepted. Deep Neural Network (DNN) performs the proposed authentication process. A DNN is a multilayered artificial neural network between the layers of input and output. The DNN finds the right mathematical manipulation to turn the input into the output, whether it is an acknowledged image or not. Suggested process quality is measured in terms of reliability recognition. In the MATLAB platform, the suggested approach is implemented.