Twins Recognition Using Hierarchical Score Level Fusion

TL;DR

This paper presents a hierarchical score level fusion approach for multimodal biometric recognition of twins, combining ear and voice data with classical and deep learning algorithms to improve identification accuracy.

Contribution

It introduces a novel hierarchical fusion method for multimodal biometric data specifically targeting twin recognition, enhancing accuracy over individual systems.

Findings

Achieved 94.74% rank-1 recognition rate.

Attained 100% rank-2 recognition rate.

Demonstrated effectiveness of combining classical and deep learning methods.

Abstract

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.