Abstract:
The ear is a visible organ with a unique structure for each person. As a result, it can be used as a biometric to circumvent
the constraints of person identification. Deep learning methods like You Only Look Once (YOLO) and MobileNet have recently
significantly aided real-time biometric recognition. As a result, in this paper, we approach identifying a person using YOLOV3,
YOLOV5, MobileNet-SSDV1, and MobileNet-SSDV2 deep learning algorithms using their ear biometrics. The used ear biometric is
a standard dataset (EarVN1.0 Dataset) from 164 individuals with a total of 27,592 images. We chose 10 people at random, totaling
2057 pictures. Of these, 85% were used for training, 5% for validation, and 10% for testing. The performance of the algorithms is
determined based on their accuracy and how smoothly the ear of a person is detected. The training accuracy of the algorithms is
thresholded at 99.87%. MobileNet-SSDV1, MobileNet-SSDV2, YOLOV3, and YOLOV5 have testing accuracy that is 88%, 91%, 95%,
and 96%, respectively. We concluded that the YOLOV5 model outperforms the others in terms of accuracy and size (16MB) for person
identification using ear biometrics.
