Enhanced gesture-based human-computer interaction through a Compressive Sensing reduction scheme of very large and efficient depth feature descriptors

In this paper, a hand gesture-based recognition system is presented with the aim of recognizing finger-spelling using the American Sign Language. The solution makes use of the depth imagery acquired by the new Kinect 2 sensor that provides more depth resolution. The main novelty is the introduction of a Compressive Sensing step to reduce the dimension of a depth-based feature descriptor, called Depth Spatiograms of Quantized Patterns, which is very discriminative, but also too large for its practical application. The system is composed by three steps: 1) depth-based feature descriptor computation that robustly characterizes the hand gesture; 2) Compressive Sensing based dimensionality reduction that shortens the previous highly discriminative but also large feature vector with almost no information lost; and 3) Support Vector Machine based classification that recognizes the performed hand gestures. Promising recognition results have been obtained in an American Sign Language based database.