Offline signature verification using a region based deep metric learning network

doi:10.1016/j.patcog.2021.108009

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 模式分析与学习团队

	Offline signature verification using a region based deep metric learning network
	Liu, Li 1; Huang, Linlin 1; Yin, Fei 2; Chen, Youbin 3
刊名	PATTERN RECOGNITION
	2021-10-01
卷号	118 页码:12
关键词	Signature verification Convolutional siamese network Deep metric learning Region fusion
ISSN号	0031-3203
DOI	10.1016/j.patcog.2021.108009
通讯作者	Huang, Linlin(huangll@bjtu.edu.cn)
英文摘要	Handwritten signature verification is a widely used biometric for person identity authentication in document forensics. Despite the tremendous effort s in past research, offline signature verification still remains a challenge, particularly in discriminating between genuine signatures and skilled forgeries, because the difference of appearance between genuine and skilled forgery may be smaller than that between genuine ones. This challenge is even more critical in writer-independent scenario, where each writer has very few samples for training. This paper proposes a region based Deep Convolutional Siamese Network using metric learning method, which is applicable to both writer-dependent (WD) and writer-independent (WI) scenario. For representing minute but discriminative details, a Mutual Signature DenseNet (MSDN) is designed to extract features and learn the similarity measure from local regions instead of whole signature images. Based on local regions comparison, the similarity scores of multiple regions are fused for final decision of verification. In experiments on public datasets CEDAR and GPDS, the proposed method achieved state-of-the-art performance of 6.74% EER and 8.24% EER in WI scenario, respectively, and 1.67% EER and 1.65% EER in WD scenario, respectively. (c) 2021 Elsevier Ltd. All rights reserved.
资助项目	Major Project for New Generation of AI[2020AAA0109702]
WOS关键词	RECOGNITION ; CLASSIFIER ; DISTANCE ; SVM
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	ELSEVIER SCI LTD
WOS记录号	WOS:000670333100015
资助机构	Major Project for New Generation of AI
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/45237]
专题	自动化研究所_模式识别国家重点实验室_模式分析与学习团队
通讯作者	Huang, Linlin
作者单位	1.Beijing Jiaotong Univ, Sch Elect & Informat, Beijing 100044, Peoples R China 2.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 3.MicroPattern Co Ltd, Dongguan, Guangdong, Peoples R China
推荐引用方式 GB/T 7714	Liu, Li,Huang, Linlin,Yin, Fei,et al. Offline signature verification using a region based deep metric learning network[J]. PATTERN RECOGNITION,2021,118:12.
APA	Liu, Li,Huang, Linlin,Yin, Fei,&Chen, Youbin.(2021).Offline signature verification using a region based deep metric learning network.PATTERN RECOGNITION,118,12.
MLA	Liu, Li,et al."Offline signature verification using a region based deep metric learning network".PATTERN RECOGNITION 118(2021):12.