On the recognition of the alphabet of the sign language through size functions

Size functions are integer-valued functions which represent both qualitative and quantitative properties of visual shape. In this paper the use of size functions for the understanding of the alphabet of sign language is described. First, a family of size functions able to capture important aspects of shape from the apparent outline of the various signs are presented and motivated. Each sign is represented by means of a feature vector computed from the proposed family of size functions. Then, a training set of feature vectors is built from real images. Finally, the k-nearest-neighbor rule is employed for the classification of feature vectors computed from previously unseen signs. The reported experiments indicate that size functions can be extremely effective for the recognition of signs even in the presence of shape changes due to difference in hands, pose, style of signing, and viewpoint.