Gesture recognition based on an improved local sparse representation classification algorithm

被引：80

作者：

He, Yang ^{[1
]}

Li, Gongfa ^{[1
,2
]}

Liao, Yajie ^{[1
]}

Sun, Ying ^{[1
,2
]}

Kong, Jianyi ^{[1
,2
]}

Jiang, Guozhang ^{[1
,2
]}

Jiang, Du ^{[1
]}

Tao, Bo ^{[1
]}

Xu, Shuang ^{[1
]}

Liu, Honghai ^{[3
]}

机构：

[1] Wuhan Univ Sci & Technol, Key Lab Met Equipment & Control Educ, Educ Minist, Wuhan 430081, Hubei, Peoples R China

[2] Wuhan Univ Sci & Technol, Hubei Key Lab Mech Transmiss & Mfg Engn, Wuhan 430081, Hubei, Peoples R China

[3] Univ Portsmouth, Sch Comp, Portsmouth PO1 3HE, Hants, England

来源：

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS | 2019年 / 22卷 / Suppl 5期

基金：

中国国家自然科学基金;

关键词：

Gesture recognition; l(2) norm; Sparse representation; Classification algorithm; INTELLIGENT CONTROL; FACE RECOGNITION; IMAGE;

D O I：

10.1007/s10586-017-1237-1

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The sparse representation classification method has been widely concerned and studied in pattern recognition because of its good recognition effect and classification performance. Using the minimized l(1) norm to solve the sparse coefficient, all the training samples are selected as the redundant dictionary to calculate, but the computational complexity is higher. Aiming at the problem of high computational complexity of the l(1) norm based solving algorithm, l(2) norm local sparse representation classification algorithm is proposed. This algorithm uses the minimum l(2) norm method to select the local dictionary. Then the minimum l(1) norm is used in the dictionary to solve sparse coefficients for classify them, and the algorithm is used to verify the gesture recognition on the constructed gesture database. The experimental results show that the algorithm can effectively reduce the calculation time while ensuring the recognition rate, and the performance of the algorithm is slightly better than KNNSRC algorithm.

引用

页码：10935 / 10946

页数：12

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