# Real-Time Hand Gesture Identification in Thermal Images

arXiv: https://arxiv.org/abs/2303.02321 · HTML: https://mlsanity.com/paper/2303.02321/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 21 references that are in the map.
- First posted on arXiv 4 Mar 2023.

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

- [Fast and Robust Hand Tracking Using Detection-Guided Optimization](https://mlsanity.com/paper/1602.04124/) (2016)
- [The Distance Transform and its Computation](https://mlsanity.com/paper/2106.03503/) (2021)

## Abstract (arXiv)

Hand gesture-based human-computer interaction is an important problem that is well explored using color camera data. In this work we proposed a hand gesture detection system using thermal images. Our system is capable of handling multiple hand regions in a frame and process it fast for real-time applications. Our system performs a series of steps including background subtraction-based hand mask generation, k-means based hand region identification, hand segmentation to remove the forearm region, and a Convolutional Neural Network (CNN) based gesture classification. Our work introduces two novel algorithms, bubble growth and bubble search, for faster hand segmentation. We collected a new thermal image data set with 10 gestures and reported an end-to-end hand gesture recognition accuracy of 97%.