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Neural Aggregation Network for Video Face Recognition
Open accessAbstract
This paper presents a Neural Aggregation Network (NAN) for video face recognition. The network takes a face video or face image set of a person with a variable number of face images as its input, and produces a compact, fixed-dimension feature representation for recognition. The whole network is composed of two modules. The feature embedding module is a deep Convolutional Neural Network (CNN) which maps each face image to a feature vector. The aggregation module consists of two attention blocks which adaptively aggregate the feature vectors to form a single feature inside the convex hull spanned by them. Due to the attention mechanism, the aggregation is invariant to the image order. Our NAN is trained with a standard classification or verification loss without any extra supervision signal, and we found that it automatically learns to advocate high-quality face images while repelling low-quality ones such as blurred, occluded and improperly exposed faces. The experiments on IJB-A, YouTube Face, Celebrity-1000 video face recognition benchmarks show that it consistently outperforms naive aggregation methods and achieves the state-of-the-art accuracy.
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“, image classification [5, 6], object detection [7, 8], semantic segmentation [9, 10], face recognition [11, 12], person re-identification [13, 14], action recognition [15, 16], few-shot learning [17, 18], medical image processing [19, 20], image generation [21, 22], pose estimation [23], super resolution [24, 25], 3D…”
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Metadata
- Authors
- Yang, J., Ren, P., Zhang, D., Chen, D., Wen, F., Li, H., & Hua, G.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 5216-5225
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963559058
- Cited by
- 409
- References
- 42
- FWCI
- 19.17
- Type
- conference paper
- Language
- en
- Open access
- green
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