LDCRFs-based hand gesture recognition

This paper proposes a system to recognize isolated American Sign Language and numbers in real-time from Bumblebee stereo camera using Latent-Dynamic Conditional Random Fields (LDCRFs). Our system is based on three main stages: preprocessing, feature extraction and classification. In preprocessing stage, color and 3D depth map are used to detect and track the hand. The second stage, combining features of location, orientation and velocity with respected to Polar systems are used. The depth information is to identify the region of interest and consequently reduces the cost of searching and increases the processing speed. In the final stage, the hand gesture path is recognized using LDCRFs, which are more restricted to the number of hidden states owned by each class label to make training and inferencing processes tractable. Experimental results demonstrate that, our system can successfully recognize gestures with 96.14% recognition rate. Such results have the potential to compare very favorably to those of other investigators published in the literature.