CASIA OpenIR  > 模式识别国家重点实验室  > 生物识别与安全技术
Neighborhood-Aware Attention Network for Semi-supervised Face Recognition
Qi Zhang1,2; Zhen Lei1,2; Stan Z. Li1,2
2020-07-19
Conference NameInternational Joint Conference on Neural Networks
Conference Date2020-07-19
Conference PlaceGlasgow, UK
Abstract

Although face recognition has achieved fairly remarkable results in recent years, it heavily relies on large-scale labeled face datasets to train the high-capacity deep convolutional neural networks. However, it is unrealistic to collect larger
labeled datasets to further boost the performance, which requires burdensome and expensive annotation efforts. Meanwhile, we have easy access to abundant unlabeled face data. It is a natural idea to jointly utilize limited labeled and abundant unlabeled data to obtain higher performance gain, which is the
target of semi-supervised learning. In this paper, we propose a unified Neighborhood-Aware Attention Network (NAAN) for semi-supervised face recognition, where the neighborhood is defined as a k-hop ego network centered in the given sample called “ego”. Considering the different importance of neighbors, we employ the graph attention network to learn the ego’s representation, which selectively attends to informative nodes in the neighborhood. With the neighborhood-aware embeddings, NAAN infers pairwise relations of unlabeled face images to cluster them. We evaluate our model on two face recognition datasets MegaFace and IJB-A, and it yields favorably comparable performance to the fully-supervised results.

Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/39251
Collection模式识别国家重点实验室_生物识别与安全技术
Corresponding AuthorZhen Lei
Affiliation1.CBSR & NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.University of Chinese Academy of Sciences, Beijing, China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Qi Zhang,Zhen Lei,Stan Z. Li. Neighborhood-Aware Attention Network for Semi-supervised Face Recognition[C],2020.
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