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

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.

Defect Identification of 316L Stainless Steel in Selective Laser Melting Process Based on Deep Learning

“…YOLOv5, as a single-stage object detection algorithm characterized by its fast detection speed, is well suited for real-time applications. YOLOv5 incorporates multiple optimization strategies, such as network structure improvements and data augmentation, to achieve accurate defect detection [18]. By employing feature fusion across different scales, YOLOv5 effectively handles defects of varying sizes and scales.…”