A Comparison of Random Forests and Dropout Nets for Sign Language Recognition with the Kinect

Abstract

Random Forests (RF) and Dropout networks are currently two of the most effective machine learning algorithms available. However, so far a study directly comparing the accuracy of both on the same dataset has not been performed. We hope to fill this gap by testing the classification accuracy of both of these ensemble methods on a novel dataset of American Sign Language (ASL) hand signs collected using the Microsoft Kinect. Results show that dropout nets achieve a higher gesture classification accuracy, particularly as the number of classification labels increases. Further, a neural network trained with dropout outperforms the same net without dropout, demonstrating the effectiveness of the technique. Individual gesture recognition accuracy as well as computation times for both algorithms will be presented.