Hand gesture recognition using neural network based techniques

In this paper, two neural network based methods were implemented for recognition of images showing 10 hand gestures. Images were available from 24 subjects and captured on two different backgrounds and with several space orientations. Firstly, Histogram of Oriented Gradients method was applied for feature extraction and training was performed with multilayer feedforward neural network with backpropagation algorithm. Within the second method, Sparse autoencoder with 5 hidden layers and decreasing number of neurons was implemented. For both methods it was examined how number of descriptors influences the accuracy of classification and found relationship was used to determine best performing case. Both classification methods achieved accuracy of about 92.5%, by using the similar number of estimated parameters.