Comparison of YOLO (V3,V5) and MobileNet-SSD (V1,V2) for Person Identification Using Ear-Biometrics

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 (EarVN1.0Dataset) 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.