Paper Info
Title
Two-stage PCA with interpolated data for hand shape recognition in sign language
Abstract
Hand shape recognition is a challenging task because hands are deformable objects. Some techniques for hand shape recognition using Computer Vision have been proposed. The key problem is how to make hand gestures understood by computers/mobile devices. In this paper we present a study about Principal Component Analysis (PCA) used to reduce the dimensionality and extract features of images of the human hand. The dataset used in this study is the alphabet of Irish Sign Language. We propose to apply PCA in more than one stage, creating a second stage PCA with even lower dimensions. In this second stage, we interpolate data using splines. This data has missing translations. Blurring, using a Gaussian filter, is applied to these images in order to reduce the non-linearity in the manifolds within the eigenspaces. Some comparison of the influence of the number of eigenvectors and the number of points interpolated are shown. Finally, we apply k-Nearest- Neighbour (k-NN) in order to classify the correct shape and show the accuracy.
Year
DOI
Venue
2016
10.1109/AIPR.2016.8010587
2016 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)
Keywords
Field
DocType
two-stage PCA,two-stage principal component analysis,interpolated data,hand shape recognition,Irish sign language,computer vision,mobile devices,computers,dimensionality reduction,feature extraction,splines,blurring,Gaussian filter,eigenspaces,k-nearest-neighbour
Spline (mathematics),Gaussian filter,Computer vision,Pattern recognition,Computer science,Gesture,Interpolation,Gesture recognition,Curse of dimensionality,Sign language,Artificial intelligence,Principal component analysis
Conference
ISBN
Citations 
PageRank 
978-1-5090-3285-3
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Marlon Oliveira111.72
Alistair Sutherland210114.36
Mohamed Farouk3181.90