Automated Fingernail Detection: Utilizing MediaPipe for Finger Nail Outlining

Introduction

In the beauty technology sector, precision in nail services is crucial. The project "Automated Fingernail Detection using MediaPipe" focuses on accurately locating and outlining fingernails, an essential component for enhancing automated manicure processes.

Objective

The aim was to create a system that can identify and trace the contours of fingernails reliably. This task is complex due to the diversity in finger sizes, shapes, and nail styles.

Technical Approach

Integration of MediaPipe and OpenCV

Our solution leverages MediaPipe and OpenCV. MediaPipe is a framework adept at processing images and videos for feature recognition, while OpenCV provides powerful image processing tools.

Process Overview

1. Hand and Fingertip Detection: Initially, the system detects hands and fingertips using MediaPipe. This step is critical for locating the nails.
   

1. Isolating Fingertip Regions: After identifying the fingertips, the system isolates these areas to focus on the nail regions.

2. Applying Edge Detection for Nail Contours: Using OpenCV, edge detection algorithms are applied to the isolated fingertip regions. This method enables the system to outline the nails by detecting color and texture changes at the nail edges.

3. Overlaying Detection Results: The detected nail edges are overlaid on the original image, providing a visual representation of the nail boundaries.

Relevance to Automated Nail Services

This technology is particularly relevant for automated manicures and nail painting. By accurately identifying nail boundaries, it ensures precision in nail art applications, thereby enhancing service quality. Furthermore, by combining the location data of the fingernail with data collected from a LiDAR camera, we can use the fingernail boundary to accurately map the real-world coordinates of the user's fingernail. This approach allows for the exclusion of irrelevant data pertaining to the rest of the hand, focusing solely on the nail area. This integration of technologies enhances the precision and applicability of the system in real-world automated manicure scenarios.

Conclusion

This project demonstrates an approach to nail detection and outlining using MediaPipe and OpenCV, offering a practical solution for nail care automation in the beauty tech industry. It presents an opportunity to improve the precision and efficiency of automated nail services.