EMFO based Supervision Kernel Entropy Component Analysis for High Speed Encrypted Transmission Model

Abstract

Recently, Encryption technology plays a vital role in providing secure information. In the multimedia sector, several images and posts are broadcasted via the internet on daily basis; in which the images are transmitted on social media and are subjected to several security attacks. Therefore it is necessary to protect the images from illegal or forbidden access. This paper aims in developing a novel SKECA-EMFO based encrypted transmission system. Here, an effective encryption transmission system is designed using a Bayes minimum risk classifier to secure the sensitive information during the transmission processes. In addition to this, the SM4 encryption algorithm is employed to perform high speed encrypted transmission as well as to achieve intelligent recognition. The novel SKECA-EMFO approach is employed in demonstrating facial expression recognition thereby obtaining an optimal feature set. Finally, seven benchmark functions are utilized to examine and evaluate the effectiveness of the newly developed proposed approach. The comparative analysis is carried out with few approaches and the results reveal that the proposed approach provides better performances when compared with other approaches.