MC-GCN: A Multi-Scale Contrastive Graph Convolutional Network for Unconstrained Face Recognition With Image Sets

文献类型： 外文期刊

第一作者： Shi, Xiao

作者： Shi, Xiao;Chai, Xiujuan;Sun, Tan;Shi, Xiao;Chai, Xiujuan;Sun, Tan;Xie, Jiake

作者机构：

关键词： Prototypes; Face recognition; Feature extraction; Semantics; Faces; Task analysis; Media; Face recognition; image set; graph convolutional; contrastive information; multi-scale

期刊名称：IEEE TRANSACTIONS ON IMAGE PROCESSING （ 影响因子：11.041； 五年影响因子：12.762 ）

ISSN： 1057-7149

年卷期： 2022 年 31 卷

页码：

收录情况： SCI

摘要： In this paper, a Multi-scale Contrastive Graph Convolutional Network (MC-GCN) method is proposed for unconstrained face recognition with image sets, which takes a set of media (orderless images and videos) as a face subject instead of single media (an image or video). Due to factors such as illumination, posture, media source, etc., there are huge intra-set variances in a face set, and the importance of different face prototypes varies considerably. How to model the attention mechanism according to the relationship between prototypes or images in a set is the main content of this paper. In this work, we formulate a framework based on graph convolutional network (GCN), which considers face prototypes as nodes to build relations. Specifically, we first present a multi-scale graph module to learn the relationship between prototypes at multiple scales. Moreover, a Contrastive Graph Convolutional (CGC) block is introduced to build attention control model, which focuses on those frames with similar prototypes (contrastive information) between pair of sets instead of simply evaluating the frame quality. The experiments on IJB-A, YouTube Face, and an animal face dataset clearly demonstrate that our proposed MC-GCN outperforms the state-of-the-art methods significantly.

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