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 score level fusion method to achieve a success rate of 94.74% in rank-1 and 100% in rank -2.
Abstract (translated)
随着技术的发展，生物特征识别系统的使用领域和重要性开始增加。由于每个人的特征不同，一个单一的模型生物识别系统可以产生成功的结果。然而，由于双胞胎的特征彼此非常接近，多个生物特征识别系统（包括个体的多个特征）将更为合适，并将提高识别率。本研究开发了一套由多个算法和多个模型组合而成的多重生物识别系统，用以区分人与他人及其双胞胎。多模态模型采用耳和语音生物特征数据，数据集采用38对双耳图像和录音。使用经典（手工制作）和深度学习算法获得声音和耳朵识别率。将所得结果与分数水平融合法相结合，一级成功率为94.74%，二级成功率为100%。
URL
https://arxiv.org/abs/1903.07981
