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SSRFace: a face recognition framework against shallow data

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Abstract

Most existing deep-learning-based approaches rely on high-resolution large-scale datasets to improve their performance. However, obtaining such datasets is challenging for tasks such as face recognition. The best way to address this is first to address the issue deep-learning-based approaches experience when trained on limited samples or shallow datasets (i.e., lack of diversity). We propose SSRFace, a framework for face recognition on shallow datasets. In detail, SSRFace leverages two novel components: Segregate-Representation (SR) and SimInstance. SR utilizes unlabeled data and angular-margin-based loss to increase inter-class distance, improving class discrimination. SimInstance, on the other hand, has a straightforward approach to improving intra-class diversity. Our proposed SimInstance starts by learning the unique class distribution from the few samples before randomly sampling a feature representation to serve as new intra-class samples. We train our model on TinyFace, a shallow dataset, to show its capabilities. We show SSRFace performed better than other existing approaches with Rank-1 accuracy and mean average precision (mAP) when trained on shallow datasets.

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References

  1. Cao Q, Shen L, Xie W, Parkhi OM, Zisserman A (2018) Vggface2: a dataset for recognising faces across pose and age. In: 2018 13th IEEE international conference on automatic face gesture recognition (FG 2018), pp 67–74. https://doi.org/10.1109/FG.2018.00020

  2. Chandran P, Bradley D, Gross M, Beeler T (2020) Attention-driven cropping for very high resolution facial landmark detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 5861–5870

  3. Chen T, Kornblith S, Norouzi M, Hinton G (2020) A simple framework for contrastive learning of visual representations. In: International conference on machine learning. PMLR, pp 1597–1607

  4. Cheng Z, Zhu X, Gong S (2018) Low-resolution face recognition. In: Asian conference on computer vision. Springer, pp 605–621

  5. Cheng Z, Zhu X, Gong S (2018) Surveillance face recognition challenge. arXiv:1804.09691

  6. Deng J, Guo J, Xue N, Zafeiriou S (2019) Arcface: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 4690–4699

  7. Deng Y, Yang J, Xu S, Chen D, Jia Y, Tong X (2019) Accurate 3d face reconstruction with weakly-supervised learning: from single image to image set. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp 0–0

  8. Ding Z, Guo Y, Zhang L, Fu Y (2019) Generative one-shot face recognition. arXiv:1910.04860

  9. Du H, Shi H, Liu Y, Wang J, Lei Z, Zeng D, Mei T (2020) Semi-siamese training for shallow face learning. In: European conference on computer vision. Springer, pp 36–53

  10. Feng Y, Wu F, Shao X, Wang Y, Zhou X (2018) Joint 3d face reconstruction and dense alignment with position map regression network. In: Proceedings of the European conference on computer vision (ECCV), pp 534–551

  11. Guo Y, Zhang L (2017) One-shot face recognition by promoting underrepresented classes. arXiv:1707.05574

  12. Guo Y, Zhang L, Hu Y, He X, Gao J (2016) Ms-celeb-1m: a dataset and benchmark for large-scale face recognition. arXiv:1607.08221

  13. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  14. He K, Fan H, Wu Y, Xie S, Girshick R (2020) Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 9729–9738

  15. Hsu C-H, Hua K-L, Cheng W-H (2013) Physiognomy master: a novel personality analysis system based on facial features. In: Proceedings of the 21st ACM international conference on multimedia, pp 407–408

  16. Hu M-C, Wu H-T, Lo L-Y, Pan T-Y, Cheng W-H, Hua K-L, Mei T (2016) A framework of enlarging face datasets used for makeup face analysis. In: 2016 IEEE second international conference on multimedia big data (BigMM), pp 219–222. https://doi.org/10.1109/BigMM.2016.62

  17. Huang GB, Mattar M, Berg T, Learned-Miller E (2008) Labeled faces in the wild: a database forstudying face recognition in unconstrained environments. In: Workshop on faces in’Real-Life’Images: detection, alignment, and recognition

  18. Khalid SS, Awais M, Feng Z-H, Chan C-H, Farooq A, Akbari A, Kittler J (2020) Resolution invariant face recognition using a distillation approach. IEEE Trans Biom Behav Identity Sci 2(4):410–420

    Article  Google Scholar 

  19. Li W, Guo T, Li P, Chen B, Wang B, Zuo W, Zhang L (2021) Virface: enhancing face recognition via unlabeled shallow data. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 14729–14738

  20. Liu W, Wen Y, Yu Z, Li M, Raj B, Song L (2017) Sphereface: deep hypersphere embedding for face recognition. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 6738–6746. https://doi.org/10.1109/CVPR.2017.713

  21. Liu Y, Wei F, Shao J, Sheng L, Yan J, Wang X (2018) Exploring disentangled feature representation beyond face identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2080–2089

  22. Liu Z, Zhu X, Hu G, Guo H, Tang M, Lei Z, Robertson NM, Wang J (2019) Semantic alignment: finding semantically consistent ground-truth for facial landmark detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 3467–3476

  23. Marzani G, Suciati N, Hidayati SC (2021) Morphological preprocessing for low-resolution face recognition using common space. In: 2021 international conference on ICT for smart society (ICISS). IEEE, pp 1–5

  24. Massoli FV, Amato G, Falchi F (2020) Cross-resolution learning for face recognition. Image Vision Comput 99:103927

    Article  Google Scholar 

  25. Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 815–823

  26. Sengupta S, Chen J-C, Castillo C, Patel VM, Chellappa R, Jacobs DW (2016) Frontal to profile face verification in the wild. In: 2016 IEEE winter conference on applications of computer vision (WACV). IEEE, pp 1–9

  27. Shi Y, Yu X, Sohn K, Chandraker M, Jain AK (2020) Towards universal representation learning for deep face recognition. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 6817–6826

  28. Sohn K (2016) Improved deep metric learning with multi-class n-pair loss objective. In: Advances in neural information processing systems, pp 1857–1865

  29. Sun Y, Chen Y, Wang X, Tang X (2014) Deep learning face representation by joint identification-verification, vol 27

  30. Tan DS, Soeseno JH, Hua K-L (2021) Controllable and identity-aware facial attribute transformation. IEEE Trans Cybern:1–12. https://doi.org/10.1109/TCYB.2021.3071172

  31. Wang L, Li Y, Wang S (2018) Feature learning for one-shot face recognition. In: 2018 25th IEEE international conference on image processing (ICIP). IEEE, pp 2386–2390

  32. Wang F, Liu W, Liu H, Cheng J (2018) Additive margin softmax for face verification. arXiv:1801.05599

  33. Wang H, Wang Y, Zhou Z, Ji X, Gong D, Zhou J, Li Z, Liu W (2018) Cosface: large margin cosine loss for deep face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5265–5274

  34. Wu Y, Liu H, Fu Y (2017) Low-shot face recognition with hybrid classifiers. In: Proceedings of the IEEE international conference on computer vision workshops, pp 1933–1939

  35. Yang L, Zhan X, Chen D, Yan J, Loy CC, Lin D (2019) Learning to cluster faces on an affinity graph. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 2298–2306

  36. Yang L, Chen D, Zhan X, Zhao R, Loy CC, Lin D (2020) Learning to cluster faces via confidence and connectivity estimation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 13369–13378

  37. Yao X, Han J, Cheng G, Qian X, Guo L (2016) Semantic annotation of high-resolution satellite images via weakly supervised learning. IEEE Trans Geosci Remote Sens 54(6):3660–3671

    Article  Google Scholar 

  38. Yeo Y-Y, See J, Wong L-K, Goh H-N (2021) Generating aesthetic based critique for photographs. In: 2021 IEEE international conference on image processing (ICIP). IEEE, pp 2523–2527

  39. Yi D, Lei Z, Liao S, Li SZ (2014) Learning face representation from scratch. arXiv:1411.7923

  40. Yu H, Fan Y, Chen K, Yan H, Lu X, Liu J, Xie D (2019) Unknown identity rejection loss: utilizing unlabeled data for face recognition. In: Proceedings of the IEEE/CVF international conference on computer vision workshops, pp 0–0

  41. Zhan X, Liu Z, Yan J, Lin D, Loy CC (2018) Consensus-driven propagation in massive unlabeled data for face recognition. In: Proceedings of the european conference on computer vision (ECCV), pp 568–583

  42. Zheng T, Deng W (2018) Cross-pose lfw: a database for studying cross-pose face recognition in unconstrained environments. Beijing University of Posts and Telecommunications, Tech. Rep, 5:7

  43. Zheng T, Deng W, Hu J (2017) Cross-age lfw: a database for studying cross-age face recognition in unconstrained environments. arXiv:1708.08197

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Funding

Ministry of Education, Taiwan and Ministry of Science and Technology, Taiwan (Grant number: MOST109-2221-E-011-125-MY3 and MOST110-2923-E-011-004); Wang Jhan-Yang Charitable Trust Fund (Contract No. WJY 2020-HR-01).

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Keelung Rd, Taipei, 106, Taiwan

    Yun-Ting Zhou, Jilyan Bianca Dy, Shang-Che Hsu, Yu-Ling Hsu, Chao-Lung Yang & Kai-Lung Hua

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  1. Yun-Ting Zhou
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  2. Jilyan Bianca Dy
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  4. Yu-Ling Hsu
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Correspondence to Kai-Lung Hua.

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Zhou, YT., Dy, J.B., Hsu, SC. et al. SSRFace: a face recognition framework against shallow data. Multimed Tools Appl 82, 18617–18633 (2023). https://doi.org/10.1007/s11042-022-14096-0

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