Hand Detection by Two-Level Segmentation with Double-Tracking and Gesture Recognition Using Deep-Features
Abstract
Vision-based hand gesture recognition involves a visual analysis of handshape, position and/or movement. Most of the previous approaches require complex gesture representation as well as the selection of robust features for proper gesture recognition. To eliminate the problem of illumination variation and occlusion in gesture videos, a simple model-based framework has been presented here using a deep network for hand gesture recognition. The model is fed with `hand-trajectory-based-contour-images'. These images represent the motion trajectory of the hand for isolated trajectory gestures obtained via pre-processing steps—a two-level segmentation process and a double-tracking system. Deep features extracted from these images are used for estimating the hand gestures. Conventional machine learning methods involve tedious feature engineering schemes, while deep learning approaches can learn image features hierarchically from local to global with multiple layers of abstraction from a vast number of raw sample images. The feature learning capability of CNN architecture has been used here and it has shown outstanding results on three different datasets.
- Publication:
-
Sensing and Imaging
- Pub Date:
- December 2022
- DOI:
- 10.1007/s11220-022-00379-1
- Bibcode:
- 2022SenIm..23....9S
- Keywords:
-
- Hand gesture recognition;
- Human-computer interaction;
- Convolutional neural network;
- Hand-trajectory-based-contour-images;
- Double-tracking system;
- Two-level segmentation process