A Dynamic Gesture Recognition System for the Korean Sign Language (KSL) (Kim, Jang & Bien)

Summary:

This work describes a method to recognize gestures with a fuzzy min-max neural network for online recognition. They focus on recognizing Korean Sign Language (KSL) gestures, which are generally two-handed gestures, and most of the 6000 gestures in the language are made up of combinations of basic gestures, so they chose 25 important gestures for their study. They use a VPL Data-Glove, which has 10 flex angles for the fingers of a hand, (x, y, z) position, and roll, pitch, yaw. They found 10 basic direction types of motion patterns, which mostly seem to be motion in a straight line, and one case each of an arc and a circle. They use a Fuzzy Min-Max Neural Network to recognize gestures, and get nearly 85% accuracy.

Discussion:

The idea of 10 basic types of direction is interesting from a 3D gesture-based LADDER equivalent idea. Does this hold true in ASL, that there tends to be only a certain set of general, simple motions, rather than complicated curves, that could be used to describe most gestures? Then there would seem to be hope for creating a system that lets a user describe gestures in terms of those simple motions.