期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2022
卷号：13
期号：4
DOI：10.14569/IJACSA.2022.0130494
语种：English
出版社：Science and Information Society (SAI)
摘要：To build a competitive global view from multiple views which will represent all the views within a class label is the primary objective of this work. The first phase involves the extraction of spatio temporal features from videos of skeletal sign language using a 3D convolutional neural network. In phase two, the extracted spatio temporal features are ensembled into a latent low dimensional subspace for embedding in the global view. This is achieved by learning the weights of the linear combination of Laplacian eigenmaps of multiple views. Subsequently, the constructed global view is applied as training data for sign language recognition.
关键词：Laplacian eigenmaps; 3D convolutional networks; sign language recognition; multi view; skeletal data
