Gestimator: Shape and Stroke Similarity Based Gesture Recognition

Abstract

Template-based approaches are currently the most popular gesture recognition solution for interactive systems as they provide accurate and runtime efficient performance in a wide range of applications. The basic idea in these approaches is to measure similarity between a user gesture and a set of pre-recorded templates, and to determine the appropriate gesture type using a nearest neighbor classifier. While simple and elegant, this approach performs well only when the gestures are relatively simple and unambiguous. In increasingly many scenarios, such as authentication, interactive learning, and health care applications, the gestures of interest are complex, consist of multiple sub-strokes, and closely resemble other gestures. Merely considering the shape of the gesture is not sufficient for these scenarios, and robust identification of the constituent sequence of sub-strokes is also required. The present paper contributes by introducing Gestimator, a novel gesture recognizer that combines shape and stroke-based similarity into a sequential classification framework for robust gesture recognition. Experiments carried out using three datasets demonstrate significant performance gains compared to current state-of-the-art techniques. The performance improvements are highest for complex gestures, but consistent improvements are achieved even for simple and widely studied gesture types.