Freehand Sketch-Based Authenticated Security System using Convolutional Neural Network
S. Amarnadh1, P. V. G. D. Prasad Reddy2, N. V. E. S. Murthy3
1S. Amarnadh, Assistant Professor, Department of CSE, GIT, GITAM (Deemed to be University), Visakhapatnam, India.
2Prof. P. V. G. D. Prasad Reddy, Professor, Department of CS & SE, A.U. College of Engineering, Andhra University, India.
3Prof. N. V. E. S. Murhty, Professor, Department of Mathematics, A.U. College of Science and Technology, Visakhapatnam, India.
Manuscript received on November 21, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 3405-3411 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B4412129219/2019©BEIESP | DOI: 10.35940/ijeat.B4412.129219
Open Access | Ethics and Policies | Cite | Mendeley
© 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: An Authenticated Security System is a highly desired feature. In this paper, a FreeHand Sketch-based Authentication Security strategy is proposed for authentication purposes by allowing a user to choose one label from a collection of different labels and asking him to sketch the corresponding image for the selected label for registration to avoid mischievous registration and the sketched image gets preprocessed using adaptive threshold with Gaussian mixture and then predicted with a trained Convolutional Neural Network(CNN) data model to generate the necessary image label. The produced image label will compare with selected image label. If both are same then the details will store in the system database. The user gets login with his/her authorized details with sketch based image password. The image password gets preprocessed using adaptive threshold with Gaussian mixture and then predicted with a trained CNN model to produce the image name. The produced image name will compare with the system database for authentication. The methodology is tested with some sample input image passwords and the performance calculation is carried out using metrics like Recall and Precision. The proposed work exhibits the accuracy of approximately 85% by ensuring the authentication for the user security.
Keywords: Security, Biometric systems, Authentication, Authorization, Security Patterns, Convolutional Neural Network(CNN), Free Hand Sketch Based Authenticated Security.