A Robust Image Security System for Cloud-Based Smart Campus Using LBP and PCA

Abstract

Most of the educational institutes now days because of increased threat to the campus security have started installing the biometric security support system for personal identification. In real time situation, a large number of biometric data get accumulated which could be an unbearable burden for the biometric security system of a digital smart campus. This had attracted the research community in the field of computer vision and security to use the convenience of cloud computing and store their large data set to cloud servers. During the transform of the bulky data to the cloud source there is necessity of reducing the computational burden and storage burden on the intelligent biometric security system. In this paper a enhanced method for personal identification using biometric system in cloud environment is proposed in which first biometric images are encrypted using either block encryption technique or pixel encryption technique. Out of these encrypted images local binary pattern (LBP) features are extracted for identification or classifications due to which privacy can be preserved. Further PCA is applied on LBP which helps to reduce the computational time and data transfer time to cloud. These extracted features using LBP and PCA are used for equivalent image retrieval using histogram equalization technique for personal identification.