Optimized Multi-Model Biometric Based Human Authentication using Deep Neural Network
Rinky Ahuja1, Latika Duhan2
1Rinky Ahuja, Ph.D Scholar, Ansal University.
2Latika Duhan, Professor, Ansal University.
Manuscript received on 25 November 2019 | Revised Manuscript received on 06 December 2019 | Manuscript Published on 16 December 2019 | PP: 280-290 | Volume-8 Issue-3S3 November 2019 | Retrieval Number: C10661183S319/2019©BEIESP | DOI: 10.35940/ijrte.C1066.1183S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Biometric Authentication, Multimodal, Feature Extraction, Classification, Rider Optimization Algorithm (ROA).
Scope of the Article: Neural Information Processing