Real-time Dynamic Hand Gesture Recognition Based on Finger Features Using a Depth Sensing Camera
Date Issued
2014
Date
2014
Author(s)
Ding, Hsiang-En
Abstract
In recent years, people have tried to find more efficient ways to replace the old-fashioned keyboards and mice in communication between humans and computers. Among several attempts in this direction, gestures have received considerable attention as they already serve as a natural form of human interaction. The use of gestures in human-computer interaction, once only appeared in science fiction movies, has gradually become reality thanks to the advance of technologies such as multi-touch screens. The size of a touch screen, however, restricts the development of gesture recognition to a certain extent. The objective of this thesis is to develop a real-time system capable of recognizing hand gestures with a touch-less interface by taking advantage of 3D sensing capabilities of depth information.
The proposed system acquires accurate 3D data from Kinect, and use depth histograms in order to perform hand localization from any arbitrary background. The K-means clustering algorithm is used to determine the number of hands found in the image, even when occlusion occurs due to hand overlapping. In order to accommodate a diversity of gestures, we take advantage of different combinations and separations of fingertips. To cope with a variety of user habits and thickness of fingers, we use machine learning and SVM to determine the accurate amounts of fingers based on different features. Finally, a finite-state machine is used to determine the dynamic gestures of hand movements.
Subjects
即時動態手勢辨識
多點偵測
手指偵測
手指特徵擷取
K-平均分群法
Kinect感應器
三維空間深度資訊
人機互動
支持向量機器
機器學習
Type
thesis
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