Principal component pyramids for manifold learning in hand shape recognition

This paper presents two algorithms using data pyramids for hand shape recognition in Irish Sign Language. Principal Component Analysis (PCA) is used as a feature extraction and dimensionality reduction method. Originally, the problem is nonlinear and it is hard for PCA to extract the underlying structure of the data. The proposed PCA pyramids provide an alternative to nonlinear PCA as they depend on dividing the space into subspaces which are approximately linear using the appropriate eigenspace in each level. They are used to accelerate the search process to approximate the nearest neighbour search problem. The first algorithm uses unsupervised multidimensional grids to cluster the space into cells of similar objects. The second algorithm is based on training a set of simple architecture multilayer neural networks. Experimental results are given to measure the accuracy and performance of the proposed algorithms in comparison with the exhaustive search scenario. The proposed algorithms are applicable for real time applications with high accuracy measures.