OpenPose works best for the scenarios like detecting key data points from the human body, hand, facial, and foot. The key data points returned as x & y coordinates with the detection confidence can be used for further analysis.
We opted to move forward using Emgu CV with OpenPose, which provided the estimation model to derive the data points from the hand. To further improve the accuracy of hand gesture recognition in the next phase of work, we will use Pose Confidence Score as one of the data points from the Hand Pose estimation. We noticed that certain hand gestures (for example, when all the fingers of the hand are closed), the data points from the hand pose estimation are not as expected. Using this data point, we can re-estimate the hand poses that are not deemed strong enough.
Hopefully, this was a helpful prelude to your exploration of computer vision to solve complex business problems. Keep in mind, that the possibilities of application are limitless, and new technologies to accomplish these goals are introduced in the market every day. The next time we publish an article about our experience on hand gesture recognition, who knows what new solutions we may feature!