Individual Recognition with Deep Earprint Learning
Sarah O. Ali, Raid Rafi Omar Al-Nima, Emad A. Mohammed · 2021
Earprint can be considered as one of the most important biometrics specially for phone communications. The significances of this paper is represented by a new established earprint database named the Earprint Images for Northern Technical University (EINTU) and a suggested Deep Learning (DL) model for personal verification called the Deep Earprint Learning (DEL) network. The main challenge in this study is considered by establishing the EINTU database by collecting a big number of earprint images, where an acquisition device is designed and utilized. The DEL main results of 97.33% and 97.87% are attained for recognizing left and right earprints, respectively. The promising performances of this paper can motivate a future development of controlling communication mobile phone calls based on the recognized earprints.