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Cross-Batch Hard Example Mining With Pseudo Large Batch for ID vs. Spot Face Recognition

文献类型：期刊论文

作者Tan, Zichang7,8; Liu, Ajian6; Wan, Jun3,4,5; Liu, Hao3,4; Lei, Zhen2,3,4; Guo, Guodong7,8; Li, Stan Z.1,5
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2022
卷号31页码:3224-3235
关键词Face recognition Training Measurement Graphics processing units Deep learning Logic gates Feature extraction Face recognition ID vs spot deep learning cross-batch hard example mining pseudo large batch
ISSN号1057-7149
DOI10.1109/TIP.2021.3137005
通讯作者Wan, Jun(jun.wan@nlpr.ia.ac.cn)
英文摘要In our daily life, a large number of activities require identity verification, e.g., ePassport gates. Most of those verification systems recognize who you are by matching the ID document photo (ID face) to your live face image (spot face). The ID vs. Spot (IvS) face recognition is different from general face recognition where each dataset usually contains a small number of subjects and sufficient images for each subject. In IvS face recognition, the datasets usually contain massive class numbers (million or more) while each class only has two image samples (one ID face and one spot face), which makes it very challenging to train an effective model (e.g., excessive demand on GPU memory if conducting the classification on such massive classes, hardly capture the effective features for bisample data of each identity, etc.). To avoid the excessive demand on GPU memory, a two-stage training method is developed, where we first train the model on the dataset in general face recognition (e.g., MS-Celeb-1M) and then employ the metric learning losses (e.g., triplet and quadruplet losses) to learn the features on IvS data with million classes. To extract more effective features for IvS face recognition, we propose two novel algorithms to enhance the network by selecting harder samples for training. Firstly, a Cross-Batch Hard Example Mining (CB-HEM) is proposed to select the hard triplets from not only the current mini-batch but also past dozens of mini-batches (for convenience, we use batch to denote a mini-batch in the following), which can significantly expand the space of sample selection. Secondly, a Pseudo Large Batch (PLB) is proposed to virtually increase the batch size with a fixed GPU memory. The proposed PLB and CB-HEM can be employed simultaneously to train the network, which dramatically expands the selecting space by hundreds of times, where the very hard sample pairs especially the hard negative pairs can be selected for training to enhance the discriminative capability. Extensive comparative evaluations conducted on multiple IvS benchmarks demonstrate the effectiveness of the proposed method.
WOS关键词MODEL
资助项目National Key Research and Development Plan[2021YFE0205700] ; External Cooperation Key Project of Chinese Academy Sciences[173211KYSB20200002] ; Chinese National Natural Science Foundation[62106264] ; Chinese National Natural Science Foundation[61876179] ; Chinese National Natural Science Foundation[61961160704] ; Key Project of the General Logistics Department[AWS17J001] ; Science and Technology Development Fund of Macau[0008/2019/A1] ; Science and Technology Development Fund of Macau[0025/2019/AKP] ; Science and Technology Development Fund of Macau[0070/2020/AMJ]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000794186400003
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Plan ; External Cooperation Key Project of Chinese Academy Sciences ; Chinese National Natural Science Foundation ; Key Project of the General Logistics Department ; Science and Technology Development Fund of Macau
源URL[http://ir.ia.ac.cn/handle/173211/49404]  
专题自动化研究所_模式识别国家重点实验室_生物识别与安全技术研究中心
通讯作者Wan, Jun
作者单位1.Westlake Univ, Sch Engn, Hangzhou 310024, Peoples R China
2.Chinese Acad Sci, Hong Kong Inst Sci & Innovat, Ctr Artificial Intelligence & Robot, Hong Kong, Peoples R China
3.Univ Chinese Acad Sci UCAS, Sch Artificial Intelligence, Beijing 100190, Peoples R China
4.Chinese Acad Sci CASIA, Inst Automat, Natl Lab Pattern Recognit NLPR, Beijing 100190, Peoples R China
5.Macau Univ Sci & Technol MUST, Fac Innovat Engn, Taipa, Macao, Peoples R China
6.Macau Univ Sci & Technol MUST, Fac Innovat Engn, Macau, Peoples R China
7.Natl Engn Lab Deep Learning Technol & Applicat, Beijing 100000, Peoples R China
8.Baidu Res, Inst Deep Learning, Beijing 100000, Peoples R China
推荐引用方式
GB/T 7714		 Tan, Zichang,Liu, Ajian,Wan, Jun,et al. Cross-Batch Hard Example Mining With Pseudo Large Batch for ID vs. Spot Face Recognition[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2022,31:3224-3235.
APA Tan, Zichang.,Liu, Ajian.,Wan, Jun.,Liu, Hao.,Lei, Zhen.,...&Li, Stan Z..(2022).Cross-Batch Hard Example Mining With Pseudo Large Batch for ID vs. Spot Face Recognition.IEEE TRANSACTIONS ON IMAGE PROCESSING,31,3224-3235.
MLA Tan, Zichang,et al."Cross-Batch Hard Example Mining With Pseudo Large Batch for ID vs. Spot Face Recognition".IEEE TRANSACTIONS ON IMAGE PROCESSING 31(2022):3224-3235.

入库方式： OAI收割

来源：自动化研究所

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