Gesture Recognition Based on Sparse Reconstruction

被引：0

作者：

Aitimov, Askhat ^{[1
]}

Turan, Cemil ^{[1
]}

Duisebekov, Zhasdauren ^{[1
,2
]}

机构：

[1] Suleyman Demirel Univ, Dept Comp Sci, Kaskelen, Kazakhstan

[2] Satbayev Univ, Software Engn Dept, Alma Ata, Kazakhstan

来源：

2018 14TH INTERNATIONAL CONFERENCE ON ELECTRONICS COMPUTER AND COMPUTATION (ICECCO) | 2018年

关键词：

Gesture recognition; sparse representation classifier; recognition rate;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Due to the variety of feature extractions and classifiers, many different algorithms have been proposed for gesture recognition. In this paper, we work to increase the recognition performance in terms of recognition rate and execution time by using recently proposed modified sparse representation classifier based on intensity of images. Sparsity based classifier is compared with two conventional ones as K-nearest neighbor and random forest classifiers on gesture recognition. Simulation results showed that, our recognition algorithm based on sparsity has a higher performance than that of the others for both recognition rate and execution time.

引用

页数：4

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