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

doi:10.1109/TIP.2021.3137005

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 生物识别与安全技术

	Cross-Batch Hard Example Mining With Pseudo Large Batch for ID vs. Spot Face Recognition
	Tan, Zichang1,2 ; Liu, Ajian 3; Wan, Jun4,5,6 ; Liu, Hao5,6 ; Lei, Zhen5,6,7 ; Guo, Guodong 1,2; Li, Stan Z.4,8
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
	2022
卷号	31 页码:3224-3235
通讯作者	Wan, Jun(jun.wan@nlpr.ia.ac.cn)
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.
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
DOI	10.1109/TIP.2021.3137005
关键词[WOS]	MODEL
收录类别	SCI
语种	英语
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]
项目资助者	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
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000794186400003
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：3[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/173211/49405
专题	多模态人工智能系统全国重点实验室_生物识别与安全技术
通讯作者	Wan, Jun
作者单位	1.Baidu Res, Inst Deep Learning, Beijing 100000, Peoples R China 2.Natl Engn Lab Deep Learning Technol & Applicat, Beijing 100000, Peoples R China 3.Macau Univ Sci & Technol MUST, Fac Innovat Engn, Macau, Peoples R China 4.Macau Univ Sci & Technol MUST, Fac Innovat Engn, Taipa, Macao, Peoples R China 5.Chinese Acad Sci CASIA, Inst Automat, Natl Lab Pattern Recognit NLPR, Beijing 100190, Peoples R China 6.Univ Chinese Acad Sci UCAS, Sch Artificial Intelligence, Beijing 100190, Peoples R China 7.Chinese Acad Sci, Hong Kong Inst Sci & Innovat, Ctr Artificial Intelligence & Robot, Hong Kong, Peoples R China 8.Westlake Univ, Sch Engn, Hangzhou 310024, Peoples R China
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	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.