Abstract
Sign gesture recognition is the field that models sign gestures in order to facilitate communication with hearing and speech impaired people. Sign gestures are recorded with devices like a video camera or a depth camera. Palm gestures are also recorded with the Leap motion sensor. In this paper, we address palm sign gesture recognition using the Leap motion sensor. We extract geometric features from Leap motion recordings. Next, we encode the Genetic Algorithm (GA) for feature selection. Genetically selected features are fed to different classifiers for gesture recognition. Here we have used Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) classifiers to have their comparative results. The gesture recognition accuracy of 74.00% is recorded with RF classifier on the Leap motion sign gesture dataset.
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