  With the development of technology, the usage areas and importance of
biometric systems have started to increase. Since the characteristics of each
person are different from each other, a single model biometric system can yield
successful results. However, because the characteristics of twin people are
very close to each other, multiple biometric systems including multiple
characteristics of individuals will be more appropriate and will increase the
recognition rate. In this study, a multiple biometric recognition system
consisting of a combination of multiple algorithms and multiple models was
developed to distinguish people from other people and their twins. Ear and
voice biometric data were used for the multimodal model and 38 pair of twin ear
images and sound recordings were used in the data set. Sound and ear
recognition rates were obtained using classical (hand-crafted) and deep
learning algorithms. The results obtained were combined with the hierarchical
score level fusion method to achieve a success rate of 94.74% in rank-1 and
100% in rank -2.
