No abstract

Person recognition using thermal imaging, multi-biometric traits, with groups of feature filters and classifiers, is the subject of this paper. These were used to tackle the problems of biometric systems, such as a change in illumination and spoof attacks. Using a combination of, hard and soft-biometric, attributes in thermal facial images. The hardbiometric trait, of the shape of a head, was combined with soft-biometric traits such as the face wearing glasses, face wearing a cap/headgear, face with facial hairs, plain face, female face, and male face. These were experimented with, using images from Carl's database and Terravic Facial Infrared Database, and used to train clusters of neural network algorithms for each biometric trait. These comprised Restricted Boltzmann Machines (RBM), Deep Belief Networks (DBN), and Feed Forward Neural Networks (FFNN). After feature extraction, using Fast Wavelet Transform (FWT), and Linear Discriminant Analysis (LDA). A classification error of 0