Robust Hand Gesture Recognition Using HOG-9ULBP Features and SVM Model

doi:10.3390/electronics11070988

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进机器人控制团队

	Robust Hand Gesture Recognition Using HOG-9ULBP Features and SVM Model
	Li, Jianyong 4; Li, Chengbei 4; Han, Jihui 4; Shi, Yuefeng 4; Bian, Guibin 2,3; Zhou, Shuai 1,2
刊名	ELECTRONICS
	2022-04-01
卷号	11 期号:7 页码:15
关键词	hand gesture recognition support vector machines feature extraction image classification
DOI	10.3390/electronics11070988
通讯作者	Han, Jihui(hanjihui@zzuli.edu.cn)
英文摘要	Hand gesture recognition is an area of study that attempts to identify human gestures through mathematical algorithms, and can be used in several fields, such as communication between deaf-mute people, human-computer interaction, intelligent driving, and virtual reality. However, changes in scale and angle, as well as complex skin-like backgrounds, make gesture recognition quite challenging. In this paper, we propose a robust recognition approach for multi-scale as well as multi-angle hand gestures against complex backgrounds. First, hand gestures are segmented from complex backgrounds using the single Gaussian model and K-means algorithm. Then, the HOG feature and an improved 9ULBP feature are fused into the HOG-9ULBP feature, which is invariant in scale and rotation and enables accurate feature extraction. Finally, SVM is adopted to complete the hand gesture classification. Experimental results show that the proposed method achieves the highest accuracy of 99.01%, 97.50%, and 98.72% on the self-collected dataset, the NUS dataset, and the MU HandImages ASL dataset, respectively.
资助项目	Key Scientific Research Project of Colleges and Universities in Henan Province[20A120011] ; Henan Province Science and Technology Key Project[222102210233] ; National Natural Science Foundation of China[11947058] ; National Natural Science Foundation of China[61973103] ; National Natural Science Foundation of China[61473265] ; National Natural Science Foundation of China[61803344] ; Outstanding Foreign Scientist Support Project in Henan Province of China[GZS2019008]
WOS关键词	MULTIRESOLUTION GRAY-SCALE ; CLASSIFICATION ; HOG ; HISTOGRAM ; POSTURES ; SYSTEM
WOS研究方向	Computer Science ; Engineering ; Physics
语种	英语
出版者	MDPI
WOS记录号	WOS:000781535300001
资助机构	Key Scientific Research Project of Colleges and Universities in Henan Province ; Henan Province Science and Technology Key Project ; National Natural Science Foundation of China ; Outstanding Foreign Scientist Support Project in Henan Province of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/48258]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
通讯作者	Han, Jihui
作者单位	1.Zhongxing Telecommun Equipment Corp, Xian 710114, Peoples R China 2.Zhengzhou Univ, Sch Elect Engn, Zhengzhou 450001, Peoples R China 3.Chinese Acad Sci CASIA, Inst Automat, Beijing 100190, Peoples R China 4.Zhengzhou Univ Light Ind, Coll Comp & Commun Engn, Zhengzhou 450001, Peoples R China
推荐引用方式 GB/T 7714	Li, Jianyong,Li, Chengbei,Han, Jihui,et al. Robust Hand Gesture Recognition Using HOG-9ULBP Features and SVM Model[J]. ELECTRONICS,2022,11(7):15.
APA	Li, Jianyong,Li, Chengbei,Han, Jihui,Shi, Yuefeng,Bian, Guibin,&Zhou, Shuai.(2022).Robust Hand Gesture Recognition Using HOG-9ULBP Features and SVM Model.ELECTRONICS,11(7),15.
MLA	Li, Jianyong,et al."Robust Hand Gesture Recognition Using HOG-9ULBP Features and SVM Model".ELECTRONICS 11.7(2022):15.