# Feature Identification and Matching for Hand Hygiene Pose

arXiv: https://arxiv.org/abs/2108.06537 · HTML: https://mlsanity.com/paper/2108.06537/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 8 references that are in the map.
- First posted on arXiv 14 Aug 2021.

## Builds on (most cited first, 2 of 2)

- [Image Matching Using SIFT, SURF, BRIEF and ORB: Performance Comparison for Distorted Images](https://mlsanity.com/paper/1710.02726/) (2017)
- [Feature Detection for Hand Hygiene Stages](https://mlsanity.com/paper/2108.03015/) (2021)

## Abstract (arXiv)

Three popular feature descriptors of computer vision such as SIFT, SURF, and ORB compared and evaluated. The number of correct features extracted and matched for the original hand hygiene pose-Rub hands palm to palm image and rotated image. An accuracy score calculated based on the total number of matches and the correct number of matches produced. The experiment demonstrated that ORB algorithm outperforms by giving the high number of correct matches in less amount of time. ORB feature detection technique applied over handwashing video recordings for feature extraction and hand hygiene pose classification as a future work. OpenCV utilized to apply the algorithms within python scripts.