A Multichannel Convolutional Neural Network for Hand Posture Recognition

Natural communication between humans involves hand gestures, which has an impact on research in human-robot interaction. In a real-world scenario, understanding human gestures by a robot is hard due to several challenges like hand segmentation. To recognize hand postures this paper proposes a novel convolutional implementation. The model is able to recognize hand postures recorded by a robot camera in real-time, in a real-world application scenario. The proposed model was also evaluated with a benchmark database and showed better results than the ones reported in the benchmark paper.

@InProceedings{BMWW14, 
 	 author =  {Barros, Pablo and Magg, Sven and Weber, Cornelius and Wermter, Stefan},  
 	 title = {A Multichannel Convolutional Neural Network for Hand Posture Recognition}, 
 	 booktitle = {Proceedings of the 24th International Conference on Artificial Neural Networks (ICANN 2014)},
 	 number = {},
 	 volume = {},
 	 pages = {403--410},
 	 year = {2014},
 	 month = {Sep},
 	 publisher = {Springer Heidelberg},
 	 doi = {10.1007/978-3-319-11179-7_51}, 
 }