Real-time Vision-based Hand Gesture Recognition Using Haar-like Features

TLDR

The paper proposes a two‑level approach for real‑time vision‑based hand gesture classification. The method uses Haar‑like features and AdaBoost for posture recognition, then parses composite gestures with a context‑free grammar‑based syntactic analysis. The approach achieves real‑time performance with high recognition accuracy.

Abstract

This paper proposes a two level approach to solve the problem of real-time vision-based hand gesture classification. The lower level of the approach implements the posture recognition with Haar-like features and the AdaBoost learning algorithm. With this algorithm, real-time performance and high recognition accuracy can be obtained. The higher level implements the linguistic hand gesture recognition using a context-free grammar-based syntactic analysis. Given an input gesture, based on the extracted postures, the composite gestures can be parsed and recognized with a set of primitives and production rules.