Deep Feature Fusion for Iris and Periocular Biometrics on Mobile Devices

doi:10.1109/TIFS.2018.2833033

CASIA OpenIR > 模式识别实验室

	Deep Feature Fusion for Iris and Periocular Biometrics on Mobile Devices
	Zhang, Qi 1,2; Li, Haiqing2 ; Sun, Zhenan2,3 ; Tan, Tieniu2,3
发表期刊	IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
ISSN	1556-6013
	2018-11-01
卷号	13 期号:11 页码:2897-2912
通讯作者	Sun, Zhenan(znsun@nlpr.ia.ac.cn)
摘要	The quality of iris images on mobile devices is significantly degraded due to hardware limitations and less constrained environments. Traditional iris recognition methods cannot achieve high identification rate using these low- quality images. To enhance the performance of mobile identification, we develop a deep feature fusion network that exploits the complementary information presented in iris and periocular regions. The proposed method first applies maxout units into the convolutional neural networks (CNNs) to generate a compact representation for each modality and then fuses the discriminative features of two modalities through a weighted concatenation. The parameters of convolutional filters and fusion weights are simultaneously learned to optimize the joint representation of iris and periocular biometrics. To promote the iris recognition research on mobile devices under near-infrared (NIR) illumination, we publicly release the CASIA-Iris-Mobile-V1.0 database, which in total includes 11 000 NIR iris images of both eyes from 630 Asians. It is the largest NIR mobile iris database as far as we know. On the newly built CASIA-Iris-M1-S3 data set, the proposed method achieves 0.60% equal error rate and 2.32% false non-match rate at false match rate = 10(-5), which are obviously better than unimodal biometrics as well as traditional fusion methods. Moreover, the proposed model requires much fewer storage spaces and computational resources than general CNNs.
关键词	Iris recognition periocular recognition deep feature fusion adaptive weights mobile devices
DOI	10.1109/TIFS.2018.2833033
关键词[WOS]	RECOGNITION ; FACE ; AUTHENTICATION
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFB0801900] ; National Natural Science Foundation of China[61573360] ; National Natural Science Foundation of China[61427811] ; National Natural Science Foundation of China[61427811] ; National Natural Science Foundation of China[61573360] ; National Key Research and Development Program of China[2017YFB0801900]
项目资助者	National Natural Science Foundation of China ; National Key Research and Development Program of China
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000433909100014
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：93[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/27942
专题	模式识别实验室
通讯作者	Sun, Zhenan
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Ctr Res Intelligent Percept & Comp, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Zhang, Qi,Li, Haiqing,Sun, Zhenan,et al. Deep Feature Fusion for Iris and Periocular Biometrics on Mobile Devices[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,2018,13(11):2897-2912.
APA	Zhang, Qi,Li, Haiqing,Sun, Zhenan,&Tan, Tieniu.(2018).Deep Feature Fusion for Iris and Periocular Biometrics on Mobile Devices.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,13(11),2897-2912.
MLA	Zhang, Qi,et al."Deep Feature Fusion for Iris and Periocular Biometrics on Mobile Devices".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 13.11(2018):2897-2912.