A new entropy function and a classifier for thermal face recognition

An attempt is made to devise a new entropy function that goes beyond the existing entropy functions with its ability to change the information source values (gray levels in an IR image) and its information gain by selecting its parameters. Our objective is to improve the existing results on the Infra-Red thermal face recognition by using this entropy function that possesses peculiar characteristics such as splitting and inverting which impart a discriminating power. To cash on its discriminating power, two types of features Effective Gaussian Information (EGI) source and Effective Exponential Information (EEI) source functions are developed. To classify the features, we have modified our earlier classifier (Mamta and Hanmandlu, 2014) using the new entropy function. The performance of the new features and new classifier is tested on IR face databases under the constrained and the unconstrained conditions with regard to occlusion, noise and low resolution. A comparison of performance shows that the new entropy function outperforms the existing entropy functions such as Shannon, Renyi, Tsallis and Pal and Pal, Collision, Min entropy and Susan–Hanman.