Static Hand Gesture Recognition Using Principal Component Analysis Combined with Artificial Neural Network
Trong-Nguyen Nguyen1, Huu-Hung Huynh1, and Jean Meunier2
1.DATIC, Department of Computer Science, University of Science and Technology, Danang, Vietnam
2.DIRO, University of Montreal, Montreal, Canada
2.DIRO, University of Montreal, Montreal, Canada
Abstract—Sign language is the primary language used by the deaf community in order to convey information through gestures instead of words. In addition, this language is also used for human-computer interaction. In this paper, we propose an approach which can recognize sign language, based on principal component analysis and artificial neural network. Our approach begins by detecting the hand, pre-processing, determining eigenspace to extract features and using artificial neural network for training and testing. This method has low computational cost and can be applied in real-time. The proposed approach has been tested with high accuracy and is promising.
Index Terms—sign language, gesture, skin color, PCA, eigenspace, eigenvalue, eigenvector
Cite: Trong-Nguyen Nguyen, Huu-Hung Huynh, and Jean Meunier, "Static Hand Gesture Recognition Using Principal Component Analysis Combined with Artificial Neural Network," Jounal of Automation and Control Engineering, Vol. 3, No. 1, pp. 40-45, February, 2015. doi: 10.12720/joace.3.1.40-45
Index Terms—sign language, gesture, skin color, PCA, eigenspace, eigenvalue, eigenvector
Cite: Trong-Nguyen Nguyen, Huu-Hung Huynh, and Jean Meunier, "Static Hand Gesture Recognition Using Principal Component Analysis Combined with Artificial Neural Network," Jounal of Automation and Control Engineering, Vol. 3, No. 1, pp. 40-45, February, 2015. doi: 10.12720/joace.3.1.40-45