The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
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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Cites
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Going deeper with convolutions
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 14,214
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
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Dimensionality Reduction by Learning an Invariant Mapping
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A Discriminative Feature Learning Approach for Deep Face Recognition
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Cited by
VGGFace2: A Dataset for Recognising Faces across Pose and Age
Cao, Q., Shen, L., Xie, W., Parkhi, O. M., & Zisserman, A. (2018) · 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018) · doi:10.1109/fg.2018.00020 · Cited by 3,032
Attention mechanisms in computer vision: A survey
Guo, M.-H., Xu, T.-X., Liu, J.-J., Liu, Z.-N., Jiang, P.-T., Mu, T.-J., Zhang, S.-H., Martin, R. R., Cheng, M.-M., & Hu, S.-M. (2022) · Computational Visual Media · doi:10.1007/s41095-022-0271-y · Cited by 2,566
“, 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…”
BackgroundRegion Attention Networks for Pose and Occlusion Robust Facial Expression Recognition
Wang, K., Peng, X., Yang, J., Meng, D., & Qiao, Y. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2956143 · Cited by 940
Self-citationDynamic Neural Networks: A Survey
Han, Y., Huang, G., Song, S., Yang, L., Wang, H., & Wang, Y. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3117837 · Cited by 848
A survey on deep learning based face recognition
Guo, G., & Zhang, N. (2019) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2019.102805 · Cited by 485
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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- Yang, J., Ren, P., Zhang, D., Chen, D., Wen, F., Li, H., & Hua, G. (2017). Neural Aggregation Network for Video Face Recognition. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2017.554
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