Finger Vein Biometric based Secure Access Control in Smart Home Automation
R.Sarala1, E.Yoghalakshmi2, V.Ishwarya3
1R.Sarala, Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, India.
2E. Yoghalakshmi, Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, India.
3V.Ishwarya, Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 851-855 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8044088619/2019©BEIESP | DOI: 10.35940/ijeat.F8044.088619
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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: Smart home automation has become popular with the advent of IoT technology. Smart home automation systems suffer from a number of security issues due to the vulnerabilities that exist in the different devices and the interconnection network. Providing user authentication for smart homes is an important security requirement for preventing intruders from attacking a smart home automation system. Biometric based authentication systems have been used in many applications since they provide high security than the smart cards and password based authentication systems. Finger vein recognition is a biometric authentication technique that applies pattern recognition on the images of human finger vein present beneath the skin’s surface. The advantage of using finger vein authentication is that, it is difficult to forge and also provides high accuracy as the external deformities like rashes, cracks and rough epidermis do not have an impact on the matching and recognition process. This paper deals with the implementation of a secure smart home automation system that uses finger vein biometric for the authentication mechanism. The algorithm used for authentication uses K Means Segmentation and canny edge detection for feature extraction. SVM classifier is used for the matching process. The authentication system is then incorporated into the smart home automation system that can be used to monitor and control the devices connected to it. The proposed approach shows better performance than the existing methods used in literature for authentication, monitoring and control of smart home automation systems.
Keywords: Smart home automation; User authentication; Finger vein biometric; K-means segmentation; Canny edge detection; Raspberry pi.