Knowledge Commons of Institute of Automation,CAS
Cross-Batch Hard Example Mining With Pseudo Large Batch for ID vs. Spot Face Recognition | |
Tan, Zichang1,2; Liu, Ajian3; Wan, Jun4,5,6; Liu, Hao5,6; Lei, Zhen5,6,7; Guo, Guodong1,2; 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 |
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 |
七大方向——子方向分类 | 模式识别基础 |
国重实验室规划方向分类 | 人工智能基础前沿理论 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
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. |
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Cross-Batch Hard Exa(10124KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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