Two tier feature extractions for recognition of isolated arabic sign language using fisher's linear discriminants

This paper proposes a two tier feature extraction approach for the recognition of video-based isolated Arabic sign language gestures. In the first tier, the prediction error of the image sequence is binarized and collapsed into two unidirectional accumulated differences images. In the second tier of feature extractions, two approaches are applied to the accumulated differences images: frequency domain transformation, and radon transformation. We apply such feature extractions on each of the accumulated differences images and then concatenate the resultant feature vectors. Alternatively, the accumulated differences images are concatenated prior to the second tier of feature extractions. The paper reports on the classification results of both solutions using Fisher's linear discriminants. Comparisons with existing work reveal that up to 39% of the misclassifications have been corrected.