Multibiometric identification by using ear, face, and thermal face

In this work, a secure multibiometric system is proposed. Three different biometric modalities which are ear, face, and thermal face are considered. The face and thermal face data were taken from USTC NVIE Spontaneous Database, whereas the ear data were collected from IIT Delhi Ear Image Database. For each modality, three feature extraction methods are used and four different classifiers (multilayer perceptron, decision tree, support vector machines, and probabilistic neural network) are trained by using two fusion methods which are matching score level and feature level fusion. According to the results, the individual biometrics are better for the identification problem. However, for the validation problem, both fusion methods give better false acceptance rate/false rejection rate values regarding to individual biometrics.