Gender Discrimination based on the Thermal Signature of the Face and the External Ear

Simple features extracted from the thermal infrared images of the persons' face are proposed for gender discrimination. Two different types of thermal features are used. The first type is actually based on the mean value of the pixels of specific locations on the face. All cases of persons from the used database, males and females, are correctly distinguished based on this feature. Classification results are verified using two conventional approaches, namely: a. the simplest possible neural network so that generalization is achieved along with successful discrimination between all persons and b. the leave-one-out approach to demonstrate the classification performance on unknown persons using the simplest classifiers possible. The second type takes advantage of the temperature distribution on the ear of the persons. It is found that for the men the cooler region on the ear is larger as percentage compared to that of the women.