Abstract
The advances over the past several years have pushed the performance of face recognition to an amazing level. This great success, to a large extent, is built on top of millions of annotated samples. However, as we endeavor to take the performance to the next level, the reliance on annotated data becomes a major obstacle. We desire to explore an alternative approach, namely using captioned images for training, as an attempt to mitigate this difficulty. Captioned images are widely available on the web, while the captions often contain the names of the subjects in the images. Hence, an effective method to leverage such data would significantly reduce the need of human annotations. However, an important challenge along this way needs to be tackled: the names in the captions are often noisy and ambiguous, especially when there are multiple names in the captions or multiple people in the photos. In this work, we propose a simple yet effective method, which trains a face recognition model by progressively expanding the labeled set via both selective propagation and caption-driven expansion. We build a large-scale dataset of captioned images, which contain 6.3M faces from 305K subjects. Our experiments show that using the proposed method, we can train a state-of-the-art face recognition model without manual annotation (\(99.65\%\) in LFW). This shows the great potential of caption-supervised face recognition.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amores, J.: Multiple instance classification: review, taxonomy and comparative study. Artif. Intell. 201, 81–105 (2013)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Brodley, C.E., Friedl, M.A.: Identifying mislabeled training data. CoRR (2011)
Caba Heilbron, F., Escorcia, V., Ghanem, B., Carlos Niebles, J.: Activitynet: a large-scale video benchmark for human activity understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 61–970 (2015)
Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A.: Vggface2: A dataset for recognising faces across pose and age. In: IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018) (2018)
Chen, B., Deng, W.: Weakly-supervised deep self-learning for face recognition. In: IEEE International Conference on Multimedia and Expo, ICME (2016)
Deng, J., Guo, J., Xue, N., Zafeiriou, S.: Arcface: Additive angular margin loss for deep face recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Dietterich, T.G., Lathrop, R.H., Lozano-Pérez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artif. Intell. 89, 1–2 (1997)
Gallo, I., Nawaz, S., Calefati, A., Piccoli, G.: A pipeline to improve face recognition datasets and applications. In: International Conference on Image and Vision Computing New Zealand, IVCNZ (2018)
Gao, Y., Ma, J., Yuille, A.L.: Semi-supervised sparse representation based classification for face recognition with insufficient labeled samples. IEEE Trans. Image Process. 26, 2545–2560 (2017)
Guo, S., et al.: Curriculumnet: Weakly supervised learning from large-scale web images. Lecture Notes in Computer Science (2018)
Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: MS-Celeb-1M: a dataset and benchmark for large-scale face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, Part III. LNCS, vol. 9907, pp. 87–102. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_6
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments (2008)
Huang, H., Zhang, Y., Huang, Q., Guo, Z., Liu, Z., Lin, D.: Placepedia: comprehensive place understanding with multi-faceted annotations. In: Proceedings of the European Conference on Computer Vision (ECCV) (2020)
Bosetti, G., Firmenich, S., Rossi, G., Winckler, M., Barbieri, T.: Web objects ambient: an integrated platform supporting new kinds of personal web experiences. In: Bozzon, A., Cudre-Maroux, P., Pautasso, C. (eds.) ICWE 2016. LNCS, vol. 9671, pp. 563–566. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-38791-8_49
Huang, Q., Xiong, Y., Lin, D.: Unifying identification and context learning for person recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
Huang, Q., Xiong, Y., Rao, A., Wang, J., Lin, D.: Movienet: A holistic dataset for movie understanding. In: Proceedings of the European Conference on Computer Vision (ECCV) (2020)
Huang, Q., Xiong, Y., Xiong, Y., Zhang, Y., Lin, D.: From trailers to storylines: An efficient way to learn from movies. arXiv preprint arXiv:1806.05341 (2018)
Ilse, M., Tomczak, J.M., Welling, M.: Attention-based deep multiple instance learning. arXiv preprint arXiv:1802.04712 (2018)
Jiang, L., Zhou, Z., Leung, T., Li, L., Fei-Fei, L.: Mentornet: Regularizing very deep neural networks on corrupted labels. CoRR (2017)
Jin, C., Jin, R., Chen, K., Dou, Y.: A community detection approach to cleaning extremely large face database. Comp. Int. Neurosc. 4, 24 (2018)
Kemelmacher-Shlizerman, I., Seitz, S.M., Miller, D., Brossard, E.: The megaface benchmark: 1 million faces for recognition at scale. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Klare, B.F., et al.: Pushing the frontiers of unconstrained face detection and recognition: Iarpa janus benchmark a. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28, 129–137 (1982)
Loy, C.C., et al.: Wider face and pedestrian challenge 2018: Methods and results. arXiv preprint arXiv:1902.06854 (2019)
Ng, H.W., Winkler, S.: A data-driven approach to cleaning large face datasets. In: ICIP (2014)
Patrini, G., Rozza, A., Menon, A.K., Nock, R., Qu, L.: Making deep neural networks robust to label noise: a loss correction approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
Pinheiro, P.O., Collobert, R.: From image-level to pixel-level labeling with convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
Rao, A., et al.: A unified framework for shot type classification based on subject centric lens. In: Proceedings of the European Conference on Computer Vision (ECCV) (2020)
Rao, A., et al.: A local-to-global approach to multi-modal movie scene segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020)
Roli, F., Marcialis, G.L.: Semi-supervised PCA-based face recognition using self-training. In: Yeung, D.-Y., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds.) SSPR /SPR 2006. LNCS, vol. 4109, pp. 560–568. Springer, Heidelberg (2006). https://doi.org/10.1007/11815921_61
Rolnick, D., Veit, A., Belongie, S.J., Shavit, N.: Deep learning is robust to massive label noise. CoRR (2017)
Shao, D., Xiong, Yu., Zhao, Y., Huang, Q., Qiao, Yu., Lin, D.: Find and focus: retrieve and localize video events with natural language queries. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018, Part IX. LNCS, vol. 11213, pp. 202–218. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01240-3_13
Sukhbaatar, S., Fergus, R.: Learning from noisy labels with deep neural networks. In: International Conference on Learning Representations (ICLR) Workshop (2015)
Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (2014)
Wang, F., et al.: The devil of face recognition is in the noise. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018, Part IX. LNCS, vol. 11213, pp. 780–795. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01240-3_47
Wang, H., et al.: Cosface: Large margin cosine loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
Wang, X., Yan, Y., Tang, P., Bai, X., Liu, W.: Revisiting multiple instance neural networks. Pattern Recognit. 74, 15–24 (2018)
Wang, Z., Zheng, L., Li, Y., Wang, S.: Linkage based face clustering via graph convolution network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Wen, Y., Zhang, K., Li, Z., Qiao, Yu.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, Part VII. LNCS, vol. 9911, pp. 499–515. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_31
Wu, J., Yu, Y., Huang, C., Yu, K.: Deep multiple instance learning for image classification and auto-annotation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
Xia, J., Rao, A., Huang, Q., Xu, L., Wen, J., Lin, D.: Online multi-modal person search in videos. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020, Part XII. LNCS, vol. 12357, pp. 174–190. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58610-2_11
Xiao, T., Xia, T., Yang, Y., Huang, C., Wang, X.: Learning from massive noisy labeled data for image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (2015)
Xiong, Y., Huang, Q., Guo, L., Zhou, H., Zhou, B., Lin, D.: A graph-based framework to bridge movies and synopses. In: The IEEE International Conference on Computer Vision (ICCV) (2019)
Yang, L., Chen, D., Zhan, X., Zhao, R., Loy, C.C., Lin, D.: Learning to cluster faces via confidence and connectivity estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020)
Yang, L., Huang, Q., Huang, H., Xu, L., Lin, D.: Learn to propagate reliably on noisy affinity graphs. In: Proceedings of the European Conference on Computer Vision (ECCV) (2020)
Yang, L., Zhan, X., Chen, D., Yan, J., Loy, C.C., Lin, D.: Learning to cluster faces on an affinity graph. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning face representation from scratch. arXiv preprint arXiv:1411.7923 (2014)
Zhan, X., Liu, Z., Yan, J., Lin, D., Loy, C.C.: Consensus-driven propagation in massive unlabeled data for face recognition. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 576–592. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01240-3_35
Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23, 1499–1503 (2016)
Zhao, X., Evans, N., Dugelay, J.L.: Semi-supervised face recognition with lda self-training. In: IEEE International Conference on Image Processing (2011)
Acknowledgment
This work is partially supported by the SenseTime Collaborative Grant on Large-scale Multi-modality Analysis (CUHK Agreement No. TS1610626 & No. TS1712093), the General Research Fund (GRF) of Hong Kong (No. 14203518 & No. 14205719), and Innovation and Technology Support Program (ITSP) Tier 2, ITS/431/18F.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Huang, Q., Yang, L., Huang, H., Wu, T., Lin, D. (2020). Caption-Supervised Face Recognition: Training a State-of-the-Art Face Model Without Manual Annotation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12362. Springer, Cham. https://doi.org/10.1007/978-3-030-58520-4_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-58520-4_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58519-8
Online ISBN: 978-3-030-58520-4
eBook Packages: Computer ScienceComputer Science (R0)