Two-stage PCA with interpolated data for hand shape recognition in sign language

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.