Real-Time Hand Gesture Identification in Thermal Images
This work addresses hand gesture recognition for human-computer interaction, but it is incremental as it applies existing methods to thermal data.
The authors tackled real-time hand gesture identification using thermal images, achieving 97% accuracy on a new dataset of 10 gestures.
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%.