Neighborhood-Aware Attention Network for Semi-supervised Face Recognition
Zhang, Qi1,2; Lei, Zhen1,2; Stan Z. Li1,2
2020-07-19
会议名称International Joint Conference on Neural Networks
会议日期2020-07-19
会议地点Glasgow, UK
摘要

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.

收录类别EI
七大方向——子方向分类生物特征识别
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/39251
专题模式识别国家重点实验室_生物识别与安全技术
通讯作者Lei, Zhen
作者单位1.CBSR & NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.University of Chinese Academy of Sciences, Beijing, China
第一作者单位模式识别国家重点实验室
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
Zhang, Qi,Lei, Zhen,Stan Z. Li. Neighborhood-Aware Attention Network for Semi-supervised Face Recognition[C],2020.
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