Robust Dynamic Hand Gesture Recognition System with Sparse Steric Haar-like Feature
Date Issued
2015
Date
2015
Author(s)
Liu, Chengyin
Abstract
Hand gesture is an effective and natural way for human-robot interaction (HRI) and human-computer interaction (HCI). Vision-based system is chosen so that computer can understand hand activities naturally. Recent advances in depth sensing provide opportunities for development of approaches for hand gesture understanding. Therefore, in this thesis, we presents a robust dynamic hand gesture recognition system with an RGB-D sensor. In order to automatically recognize hand gesture from color and depth image sequences, where noise and occlusion are common problems, we extract steric Haar-like features to robustly represent the complicated spatial information of the hand. A novel feature selection approach, which takes the advantage of class separability measure, is employed to effectively ferret out the most discriminative features. We also use sparse coding method to encode these features so that it is less prone to over-fitting even when only limited amount of training data are available. Generally speaking, Sparse Steric Haar-like (SSH) features are efficient to compute by using the self-padding integral volume, in addition to the advantage of robustness to noise and occlusion. These crucial features significantly improve the performance of tracking and classification. Experiments with a public dynamic hand gesture dataset and a self-built hand gesture dataset show the superiority of the proposed system compared with the state-of-the-art approaches.
Subjects
Gesture Recognition
Steric Haar-like Feature
Class Separability
Sparse Representation
Type
thesis
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