An efficient FPGA implementation of hand gestures recognition based on neural networks

Different gestures of hand which is a powerful communication channel between man to man and/or man to machine transfers a large amount of information in our daily lives. For example, sign languages are widely used by individuals with speech handicaps. Recognizing hand gestures in the image can be considered a powerful parameter in man-to-machine communication. Although researchers have been trying to implement different hand gestures on several hardware platforms over the past years, their attempts have been confronted by many challenges including restricted resources of hardware platforms, noise factors in the environment, or insufficient accuracy of output in high numbers of experimental samples. In this work, an optimum and parallelized method is developed to implement recognition of different hand gestures in the image on FPGA. The introduced method uses an MLP network with high numbers of hidden layers without wasting resources of the hardware platform. The results comparing the proposed optimized method with the state-of-the-art methods show that the suggested method can be implemented on an FPGA platform with high output accuracy and lower resources.