Skip to main content
SpringerLink
Log in

Effects of Deep Generative AutoEncoder Based Image Compression on Face Attribute Recognition: A Comprehensive Study

  • Conference paper
  • First Online:
Advances in Mobile Computing and Multimedia Intelligence (MoMM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14417))

  • 176 Accesses

Abstract

Face Attribute Recognition (FAR) is a computer vision task that has attracted a lot of attention for applications ranging from security and surveillance to healthcare. In real-world scenarios, setting up a FAR system requires an important step, which is image compression because of computational, storage, and transmission constraints. However, severe face image compression not adapted to FAR tasks can affect the accuracy of these latter. In this paper, we investigate the impact of image compression based on deep generative models on face attribute recognition performance. In particular, we present a case study on smile and gender detection by face attribute classification of compressed images. For this purpose, we use QRes-VAE (Quantized ResNet Variational AutoEncoder) for image compression, which is, to the best of our knowledge, the most powerful and efficient VAE model for lossy image compression. Unlike prior studies, we quantify the impact of using deep generative autoencoder for image compression on FAR performance. We also study the impact of varying compression rates on the FAR performance. Results obtained from experiments on the CelebA dataset highlight the potential trade-off between image compression by deep generative autoencoder and FAR performance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Benbarrad, T., Salhaoui, M., Anas, H., Arioua, M.: Impact of standard image compression on the performance of image classification with deep learning. In: Ben Ahmed, M., Boudhir, A.A., Karaş, İR., Jain, V., Mellouli, S. (eds.) SCA 2021. LNNS, vol. 393, pp. 901–911. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-94191-8_73

    Chapter  Google Scholar 

  2. Bian, N., Liang, F., Fu, H., Lei, B.: A deep image compression framework for face recognition. In: 2019 2nd China Symposium on Cognitive Computing and Hybrid Intelligence (CCHI), pp. 99–104 (2019)

    Google Scholar 

  3. Delac, K., Grgic, S., Grgic, M.: Image compression in face recognition - a literature survey. In: Delac, K., Grgic, M., Bartlett, M.S. (eds.) Recent Advances in Face Recognition, Chap. 1. IntechOpen, Rijeka (2008)

    Google Scholar 

  4. Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: International Conference on Learning Representations, May 2021

    Google Scholar 

  5. Duan, Z., Lu, M., Ma, J., Huang, Y., Ma, Z., Zhu, F.: QARV: quantization-aware ResNet VAE for lossy image compression. ArXiv abs/2302.08899 (2023)

    Google Scholar 

  6. Duan, Z., Lu, M., Ma, Z., Zhu, F.: Lossy image compression with quantized hierarchical VAEs. In: 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 198–207 (2023)

    Google Scholar 

  7. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)

    Google Scholar 

  8. Jamil, S., Piran, M.J., Rahman, M., Kwon, O.J.: Learning-driven lossy image compression: a comprehensive survey. Eng. Appl. Artif. Intell. 123, 106361 (2023)

    Article  Google Scholar 

  9. Kingma, D.P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., Welling, M.: Improved variational inference with inverse autoregressive flow. In: Advances in Neural Information Processing Systems, vol. 29, December 2016

    Google Scholar 

  10. Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11966–11976 (2022)

    Google Scholar 

  11. Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of International Conference on Computer Vision (ICCV), December 2015

    Google Scholar 

  12. Mishra, D., Singh, S.K., Singh, R.K.: Deep architectures for image compression: a critical review. Signal Process. 191, 108346 (2021)

    Article  Google Scholar 

  13. Ozah, N., Kolokolova, A.: Compression improves image classification accuracy. In: Meurs, M.-J., Rudzicz, F. (eds.) Canadian AI 2019. LNCS (LNAI), vol. 11489, pp. 525–530. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-18305-9_55

    Chapter  Google Scholar 

  14. Patel, M.I., Suthar, S., Thakar, J.: Survey on image compression using machine learning and deep learning. In: 2019 International Conference on Intelligent Computing and Control Systems (ICCS), pp. 1103–1105 (2019)

    Google Scholar 

  15. Sebai, D., Missaoui, N., Zouaghi, A.: Signal extraction for classification of noisy images compressed using autoencoders. In: International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (2021)

    Google Scholar 

  16. Shi, W., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1874–1883, June 2016

    Google Scholar 

  17. Valenzise, G., Purica, A.I., Hulusic, V., Cagnazzo, M.: Quality assessment of deep-learning-based image compression. In: 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2018)

    Google Scholar 

  18. Wang, X., Peng, J., Zhang, S., Chen, B., Wang, Y., Guo, Y.H.: A survey of face recognition. ArXiv abs/2212.13038 (2022)

    Google Scholar 

  19. Yang, Y., Mandt, S., Theis, L.: An introduction to neural data compression. Found. Trends Comput. Graph. Vis. 15(2), 113–200 (2023)

    Article  Google Scholar 

  20. Zhang, K., Tan, L., Li, Z., Qiao, Y.: Gender and smile classification using deep convolutional neural networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 739–743 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Efrei Research Lab, Paris Panthéon-Assas University, Paris, France

    Ahmed Baha Ben Jmaa

  2. Images and Forms Research Group, CRISTAL Laboratory, ENSI, University of Manouba, Manouba, Tunisia

    Dorsaf Sebai

  3. Department of Computer Science Engineering and Mathematics, INSAT, University of Carthage, Tunis, Tunisia

    Dorsaf Sebai

Authors
  1. Ahmed Baha Ben Jmaa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dorsaf Sebai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmed Baha Ben Jmaa .

Editor information

Editors and Affiliations

  1. Monash University, Melbourne, VIC, Australia

    Pari Delir Haghighi

  2. Johannes Kepler University Linz, Linz, Austria

    Ismail Khalil

  3. Johannes Kepler University Linz, Linz, Austria

    Gabriele Kotsis

  4. Udayana University, Denpasar, Indonesia

    Ngurah Agus Sanjaya ER

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ben Jmaa, A.B., Sebai, D. (2023). Effects of Deep Generative AutoEncoder Based Image Compression on Face Attribute Recognition: A Comprehensive Study. In: Delir Haghighi, P., Khalil, I., Kotsis, G., ER, N.A.S. (eds) Advances in Mobile Computing and Multimedia Intelligence. MoMM 2023. Lecture Notes in Computer Science, vol 14417. Springer, Cham. https://doi.org/10.1007/978-3-031-48348-6_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-48348-6_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48347-9

  • Online ISBN: 978-3-031-48348-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation