Online Finger Circumference Measurement System using Semantic Segmentation with Transfer Learning

Abstract

Previous methods on finger circumference measurement only have a single measurement feature provided in low accuracy. In this paper, we propose a new online finger circumference measurement system that improves both convenience and accurateness which previous methods lack. The measurement system is based on a mobile-optimized deep learning-based segmentation, DeepLabV3-MobileNetV2 pre-trained model with transfer learning, which allows us to get the finger circumference with the appropriate ring size by uploading a picture of one’s hand. It is served in the form of a progressive web application that delivers a native app-like user experience on any mobile device on top of high performance and reliability. The experimental results validate the accuracy of our approach surpassing that of the existing method and four novel features provide great convenience to users.