Hybrid Deep Network and Polar Transformation Features for Static Hand Gesture Recognition in Depth Data

Static hand gesture recognition is an interesting and challenging problem in computer vision. It is considered a significant component of Human Computer Interaction and it has attracted many research efforts from the computer vision community in recent decades for its high potential applications, such as game interaction and sign language recognition. With the recent advent of the cost-effective Kinect, depth cameras have received a great deal of attention from researchers. It promoted interest within the vision and robotics community for its broad applications. In this paper, we propose the effective hand segmentation from the full depth image that is important step before extracting the features to represent for hand gesture. We also represent the novel hand descriptor explicitly encodes the shape and appearance information from depth maps that are significant characteristics for static hand gestures. We propose hand descriptor based on Polar Transformation coordinate is called Histogram of Polar Transformation (HPT) in order to capture both shape and appearance. Beside a robust hand descriptor, a robust classification model also plays a very important role in the hand recognition model. In order to have a high performance in recognition rate, we propose hybrid model for classification based on Sparse Auto-encoder and Deep Neural Network. We demonstrate large improvements over the state-of-the-art methods on two challenging benchmark datasets are NTU Hand Digits and ASL Finger Spelling and achieve the overall accuracy as 97.7% and 84.58%, respectively. Our experiments show that the proposed method significantly outperforms state-of-the-art techniques.