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With the development of technology, the usage areas and importance ofbiometric systems have started to increase. Since the characteristics of eachperson are different from each other, a single model biometric system can yieldsuccessful results. However, because the characteristics of twin people arevery close to each other, multiple biometric systems including multiplecharacteristics of individuals will be more appropriate and will increase therecognition rate. In this study, a multiple biometric recognition systemconsisting of a combination of multiple algorithms and multiple models wasdeveloped to distinguish people from other people and their twins. Ear andvoice biometric data were used for the multimodal model and 38 pair of twin earimages and sound recordings were used in the data set. Sound and earrecognition rates were obtained using classical (hand-crafted) and deeplearning algorithms. The results obtained were combined with the score levelfusion method to achieve a success rate of 94.74% in rank-1 and 100% in rank-2.

Twins Recognition with Multi Biometric System: Handcrafted-Deep Learning Based Multi Algorithm with Voice-Ear Recognition Based Multi Modal