Uma Comparação Entre Redes Neurais Artificiais E Máquinas De Vetores De Suporte Para Reconhecimento De Posturas Manuais Em Tempo-Real

This paper presents a comparison between different artificial intelligence techniques used for finger spelling recognition on a real-time system, with emphasis on Support Vector Machines and Artificial Neural Networks but also using other wellknown methods. The comparison is based on the computational complexity, correct rate and time taken to process the required information. The tests are based in two models of ANN and a binary tree structured multi-class SVM. The results show that SVM has an equivalent recognition rate as ANN and it is computationally cheaper and faster than ANN.