Hand Postures Recognition System Using Artificial Neural Networks Implemented in FPGA

Abstract

Gesture recognition is a domain of grate interest of our days due to the multiple application possibilities, from the spatial or subaquatic robots manipulation, to the sign language used for communication by the peoples with hearing or speech disabilities. This paper shows an application of the artificial neural networks (ANN) implemented in field programmable gate arrays (FPGA) for the hand static gestures (postures) recognition. The adopted recognition method uses an ANN structured on two levels. The first level, a feedforward ANN trained using supervised Hebbian algorithm, is used for input data preprocessing. The second one, used for data classification is a competitive ANN. Using an ANN for input data preprocessing offers flexibility regarding the implementation of a preprocessing method. This combination of two ANN leads to 100% recognition rate for the training set and two other sets of test.