Dynamic hand gesture recognition based on textural features

This article proposes an implementation of a dynamic gesture recognition system using different textural descriptors such as the basic Local Binary Patterns (LBP), Rotation Invariant and Uniform LBP (LBPriu2), Center-Symmetric LBP (CS-LBP) and Edge Histogram Descriptor (EHD). The recognition task is performed using two variants of the Artificial Neural Network (ANN), which are the Multilayer Perceptron (MLP) and the Radial Basis Function neural network (RBF). Experiments were performed on a user-independent database with a simple background where 95.83% recognition rate was achieved. A comparison with previous works shows the efficiency of our system.