American sign language-based finger-spelling recognition using k-Nearest Neighbors classifier

Abstract

This paper presents finger-spelling recognition method for American Sign Language (ASL) Alphabet using k-Nearest Neighbours (k-NN) Classifier. This research also examines the effect of PCA for dimensional reduction to k-NN performance. The empiric results show that k-NN classifier achieves the highest accuracy (99.8 percent) for k=3 when the pattern is represented by full dimensional feature. However, k-NN classifier only achieves 28.6 percent accuracy (for k=5) when the pattern is represented by PCAreduced dimensional feature. This low accuracy is due to several factors, among others, is the presence of high numbers of redundant or highly correlated features among ASL alphabet that makes PCA unable to separate data. Although kNN classifier accuracy is higher than the proposed classifier in [7], recognition time of k-NN classifier is longer than that of the method proposed in [7]. Therefore, k-NN classifier is suitable for early child education-based application such as self-assessment system for special need student who learns ASL alphabet finger-spelling.