Twins Recognition with Multi Biometric System: Handcrafted-Deep Learning Based Multi Algorithm with Voice-Ear Recognition Based Multi Modal
This addresses the challenge of biometric recognition for twins, which is incremental as it applies existing methods to a specific domain.
The study tackled the problem of distinguishing individuals from their twins using a multi-biometric system combining ear and voice data, achieving a success rate of 94.74% in rank-1 and 100% in rank-2 recognition.
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 score level fusion method to achieve a success rate of 94.74% in rank-1 and 100% in rank -2.