Realtime Person Identification using Ear Biometrics
Shahadat Hossain, Shamim Akhter · 2021
Biometrics authentication is a very popular method to authorize a person to a system, device, or data. Fingerprints, Retina, Iris, Face, Voice, etc. are the most used Biometrics pattern to recognize and identify a person. A person's ear structure remains the same from his early childhood to old age as compared to other biometric organs in the human body. Thus, Ear can also be a source of a Biometric pattern to identify a person, since it is a visible organ and its image can be easily taken. In this paper, we approach identifying a person using 2D ear imaging. You Only Look Once(YOLO) machine learning (ML) algorithm is used to classify the ear images and identify their source person. We collect a standard ear dataset (EarVN1.0 Dataset) from 164 individual persons with a total of 27,592 training images. Randomly 820 Images, 5 images of each 164 individuals are selecting for testing purpose. The model training accuracy is 82.5%, and the testing accuracy is 75%. The model is implemented using the python language framework and GPU-based implementation. The model training accuracy is 82.5%, and the testing accuracy is 75%. The model is implemented using the python language framework and GPU-based implementation on Jupyter Notebook at Google Colaboratory server.