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Research on Algorithms of Lateral Face Recognition Based on Data Generation

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Advanced Computational Intelligence and Intelligent Informatics (IWACIII 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1932))

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Abstract

With the rapid progress of artificial intelligence, the methods of face recognition have also achieved considerable progress. However, when multiple head poses are present, the accuracy of face recognition decreases due to angular deviation. Therefore, how to realize the enhancement of accurate precision in two-dimensional multiple head poses is still a worthy research topic. In this paper, based on the ResNet50 residual network structure, data enhancement and attention mechanism are adopted, and the Public Figures Face Database public face database is used to partition the training and test sets. This hybrid approach effectively improved the accuracy of face recognition in lateral. However, the hybrid method has low accuracy for large angle deflection face recognition. Therefore, based on the hybrid method, this article proposes a side face recognition algorithm based on data generation, incorporating a data generation algorithm. The recognition accuracy of the front face of this algorithm is as high as 96.3%. This article presents a data-generated lateral face recognition algorithm that further improves the accuracy of large angle deflection face recognition.

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References

  1. Yansui, D.: Exploring 3D face recognition technology. Digital Technology & Applications 9, 234–236 (2017)

    Google Scholar 

  2. Lichun, G., Ming, L.: Research on face recognition algorithm for multi-gesture. Comp. Knowl. Technol. 18(21), 70–72 (2022)

    Google Scholar 

  3. Zhiyuan, L.: Overview of the current state of research on face recognition technology. Electr. Softw. Eng. 13, 106–107 (2020)

    Google Scholar 

  4. **nlin, Z.: A review of face recognition methods. Technol. Inno. Appl. 12(2), 130–132 (2022)

    Google Scholar 

  5. Xueyuan, Z.: Optimization study of face recognition method based on local feature extraction. Automation Applications 64(7), 54–56 and 60 (2023)

    Google Scholar 

  6. Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020)

    Article  MathSciNet  Google Scholar 

  7. Jie, C., Yibo, H., Hongwen, Z., et al.: Learning a high fidelity pose invariant model for high-resolution face frontalization. In: 32nd Conference on Neural Information Processing Systems, pp. 1–11. Montréal, Canada (2018)

    Google Scholar 

  8. Taherkhani, F., Talreja, V., Dawson, J., et al.: Pf-cpgan: profile to frontal coupled gan for face recognition in the wild. In: 2020 IEEE International Joint Conference on Biometrics (IJCB), pp. 1–10. IEEE, USA (2020)

    Google Scholar 

  9. Hang, Z., Jihao, L., Ziwei, L., Yu, L., **aogang, W.: Rotate-and-render: unsupervised photorealistic face rotation from single-view images. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5910–5919. USA (2020)

    Google Scholar 

  10. Jie, H., Li, S., Gang, S.: Squeeze-and-Excitation Networks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7132–7141. USA (2018)

    Google Scholar 

  11. XerCis.: Images Data Enhancement Tools: Enhancers at a Glance, https://imgaug.readthedocs.io/en/latest/. Last accessed 21 June 2023

  12. **ang, W., Kai, W., Shiguo, L.: A survey on face data augmentation for the training of deep neural networks. Neural Comput. Appl. 32(19), 15503–15531 (2020)

    Article  Google Scholar 

  13. Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-pie. Image Vis. Comput. 28, 807–813 (2010)

    Article  Google Scholar 

  14. Labeled Faces in the Wild, http://vis-www.cs.umass.edu/lfw/. Last accessed 21 June 2023

  15. Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. Proceedings of the British Machine Vision 1(3), 6 (2015)

    Google Scholar 

  16. Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning face representation from scratch. Computer Science 1–9 (2014)

    Google Scholar 

  17. Yandong, G., Lei, Z., Yuxiao, H., **aodong, H., Jianfeng, G.: Ms-celeb-1m: a dataset and benchmark for large-scale face recognition. In: Computer Vision–ECCV 2016, pp. 87–102. Springer International Publishing, Amsterdam, The Netherlands (2016)

    Google Scholar 

  18. Kumar, N., Berg, A.C., Belhumeur, P.N. Nayar, S.K.: Attribute and simile classifiers for face verification. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 365–372. Kyoto, Japan (2009)

    Google Scholar 

  19. PubFig: Public Figures Face Database, https://www.cs.columbia.edu/CAVE/databases/pubfig/explore/. Last accessed 21 June 2023

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Acknowledgements

This work was supported by the National Key Research and Development Project (2019YFB2101902).

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Authors and Affiliations

  1. School of Automation and Electrical Engineering, University of Science and Technology Bei**g, 30# Xueyuan Road, Haidian District, Bei**g, 100083, China

    Zimin Zhang, Zhaohui Zhang, **aoyan Zhao & Tianyao Zhang

  2. Bei**g Engineering Research Center of Industrial Spectrum Imaging, University of Science and Technology Bei**g, 30# Xueyuan Road, Haidian District, Bei**g, 100083, China

    Zhaohui Zhang, **aoyan Zhao & Tianyao Zhang

Authors
  1. Zimin Zhang
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  2. Zhaohui Zhang
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  3. **aoyan Zhao
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  4. Tianyao Zhang
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Corresponding author

Correspondence to Zhaohui Zhang .

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Editors and Affiliations

  1. Bei**g Institute of Technology, Bei**g, China

    Bin **n

  2. Tokyo Metropolitan University, Tokyo, Japan

    Naoyuki Kubota

  3. Ningbo University, Ningbo, China

    Kewei Chen

  4. Ningbo University, Ningbo, China

    Fangyan Dong

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zhang, Z., Zhang, Z., Zhao, X., Zhang, T. (2024). Research on Algorithms of Lateral Face Recognition Based on Data Generation. In: **n, B., Kubota, N., Chen, K., Dong, F. (eds) Advanced Computational Intelligence and Intelligent Informatics. IWACIII 2023. Communications in Computer and Information Science, vol 1932. Springer, Singapore. https://doi.org/10.1007/978-981-99-7593-8_17

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  • DOI: https://doi.org/10.1007/978-981-99-7593-8_17

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-7592-1

  • Online ISBN: 978-981-99-7593-8

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