Gesture recognition: an assessment of the performance of recurrent neural networks versus competing techniques

A gesture is a motion of the body that contains information (e.g. waving goodbye, beckoning with an index finger, signs in a sign language). There are four classes of gestures; signs (substitutes for spoken language); indications (pointing and showing direction); illustration (conveying ideas such as size and shape); and manipulation (for example making something from virtual clay). The first three of these are suitable for both input and output, while the fourth is only suitable for input. Recognition of gestures is still a major problem, and represents a challenge that rivals speech and hand-writing recognition. The paper describes a comparison of some of the competing techniques that have been applied to solving this problem. Three techniques were investigated; dynamic programming (DP), hidden Markov models (HMMs) and recurrent neural networks (RNNs). All of these techniques seek to represent time explicitly, and are therefore better suited than static techniques to the dynamic nature of most gestures. The study has application to a sign language recognition system.