Deep Feature Fusion for Iris and Periocular Biometrics on Mobile Devices
文献类型：期刊论文
| 作者 | Zhang, Qi2,3; Li, Haiqing3 ; Sun, Zhenan1,3; Tan, Tieniu1,3
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| 刊名 | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
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| 出版日期 | 2018-11-01 |
| 卷号 | 13期号:11页码:2897-2912 |
| 关键词 | Iris recognition periocular recognition deep feature fusion adaptive weights mobile devices |
| ISSN号 | 1556-6013 |
| DOI | 10.1109/TIFS.2018.2833033 |
| 通讯作者 | Sun, Zhenan(znsun@nlpr.ia.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | RECOGNITION ; FACE ; AUTHENTICATION |
| 资助项目 | National Natural Science Foundation of China[61427811] ; National Natural Science Foundation of China[61573360] ; National Key Research and Development Program of China[2017YFB0801900] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000433909100014 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; National Key Research and Development Program of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/27941]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Sun, Zhenan |
| 作者单位 | 1.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Ctr Res Intelligent Percept & Comp, Beijing 100190, Peoples R China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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