气候变化领域集成服务门户(CCPortal): Local feature fusion and SRC-based decision fusion for ear recognition

CCPortal

DOI	10.1007/s00530-022-00906-w
	Local feature fusion and SRC-based decision fusion for ear recognition
	Wang, Zhaobin; Gao, Xiong; Yang, Jing; Yan, Qizhen; Zhang, Yaonan
通讯作者	Wang, ZB (通讯作者)，Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China. ; Wang, ZB (通讯作者)，Chinese Acad Sci, Natl Cryosphere Desert Data Ctr, Northwest Inst Ecoenvironm & Resources, Lanzhou 730000, Peoples R China.
发表日期	2020
ISSN	0942-4962
EISSN	1432-1882
英文摘要	As an emerging biometric technology, human ear recognition has important applications in crime tracking, forensic identification and other fields. In the paper, we propose an effective fusion-based human ear recognition method and describe the algorithm based on three parts: preprocessing, feature extraction and classification decision. First, we employ a weighted distributed adaptive gamma correction (AGCWD)-based image enhancement method for the preprocessing operation. Features are extracted by fusing dense scale invariant feature transform (DSIFT), local binary patterns (LBP) and histogram of gradient directions (HoG), after which we apply two sparse representation-based feature selection methods, namely robust sparse linear discriminant analysis (RSLDA) and inter-class sparsity-based discriminant least square regression (ICS-DLSR), to improve the computational speed. Finally, the two selection features are classified separately using the FDDL-based SRC scheme (FDDL-based SRC), and the two sets of classification results are fused at the decision level to obtain the final decision results. Our algorithm is tested on six commonly used datasets (USTB1, USTB2, USTB3, IITD1, AMI and AWE) and obtained the accuracy of 99.44%, 97.08%, 100%, 100%, 98.14% and 82.90%. The experiments show the superiority of our algorithm compared with other algorithms.
关键词	FEATURE-EXTRACTION METHOD SPARSE REPRESENTATION BIOMETRIC SYSTEM NEURAL-NETWORKS IDENTIFICATION INVARIANT SHAPE
英文关键词	AGCWD-based preprocessing; Feature extraction; Feature selection; FDDL-based SRC
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Theory & Methods
WOS记录号	WOS:000763189800001
来源期刊	MULTIMEDIA SYSTEMS
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/254003
作者单位	[Wang, Zhaobin; Gao, Xiong; Yang, Jing; Yan, Qizhen] Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China; [Wang, Zhaobin; Zhang, Yaonan] Chinese Acad Sci, Natl Cryosphere Desert Data Ctr, Northwest Inst Ecoenvironm & Resources, Lanzhou 730000, Peoples R China
推荐引用方式 GB/T 7714	Wang, Zhaobin,Gao, Xiong,Yang, Jing,et al. Local feature fusion and SRC-based decision fusion for ear recognition[J]. 中国科学院西北生态环境资源研究院,2020.
APA	Wang, Zhaobin,Gao, Xiong,Yang, Jing,Yan, Qizhen,&Zhang, Yaonan.(2020).Local feature fusion and SRC-based decision fusion for ear recognition.MULTIMEDIA SYSTEMS.
MLA	Wang, Zhaobin,et al."Local feature fusion and SRC-based decision fusion for ear recognition".MULTIMEDIA SYSTEMS (2020).