A Static Hand Gesture Recognition Algorithm Based on Krawtchouk Moments

被引：0

作者：

Liu, Shuping ^{[1
]}

Liu, Yu ^{[1
]}

Yu, Jun ^{[1
,2
]}

Wang, Zengfu ^{[1
,2
,3
]}

机构：

[1] Univ Sci & Technol China, Dept Automat, Hefei 230026, Peoples R China

[2] Univ Sci & Technol China, Natl Engn Lab Speech & Language Informat Proc, Hefei 230026, Peoples R China

[3] Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China

来源：

PATTERN RECOGNITION (CCPR 2014), PT II | 2014年 / 484卷

关键词：

hand gesture recognition; hand detection and normalization; Krawtchouk moments; minimum distance classifier; IMAGE-ANALYSIS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Owing to convenience and naturalness, hand gesture recognition has been widely used in various human-computer interaction (HCI) systems. In this paper, we address the problem from the perspective of system, and present a static hand gesture recognition algorithm based on Krawtchouk moments. The effect of the order and number of Krawtchouk moments on the recognition performance is studied in detail. In the experiments, 15 popular gesture signs are used to verify the effectiveness of the presented hand gesture recognition system. Experimental results demonstrate that lower order Krawtchouk moments are more suitable for classification. Furthermore, the number of Krawtchouk moments also has a significant impact on the recognition accuracy.

引用

页码：321 / 330

页数：10

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