Convolutional Neural Network Based Multimodal Biometric Human Authentication using Face, Palm Veins and Fingerprint
Priti Shende1, Yogesh H. Dandwate2
1Priti Shende*, Department of E&TC, Dr. D. Y. Patil Institute of Technology, Pimpri, Pune , India.
2Yogesh H. Dandwate, Department of E&TC, Vishwakarma Institute of Technology, Pune, India
Manuscript received on December 19, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 771-777 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8467019320/2020©BEIESP | DOI: 10.35940/ijitee.C8467.019320
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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: 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.
Keywords: Convolutional Neural Network, Human Authentication, Multimodal Biometrics, Support Vector Machine
Scope of the Article: Neural Information Processing