Skip to main content
Log in

FF-PPQA: Face frontalization without glasses based on perceptual quality and pixel-level quality assessment

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Face frontalization is the process of synthesizing realistic and identity-preserving frontal views from face images in different poses and is an essential preprocessing step for face recognition. However, for side faces wearing glasses, the previous frontalization algorithms will distort the glasses after face reconstruction, affecting the image’s perceived quality and subsequent face recognition. Therefore, this paper first removes glasses, a factor that will cause distortion in face frontalization, and designs the perceptual and pixel-level face image quality assessment modules to improve the face frontalization performance. On the one hand, by constructing a saliency gradient, the pixel-level quality of face images is calculated and guides the network to generate frontal face images that are more conducive to face recognition. On the other hand, in order to obtain the perceptual quality for face image, the natural face images are used to construct a high-quality feature space, and the Bhattacharyya distance between it and the generated image is calculated to ensure the perceptual quality of the generated frontal image. Finally, the GAN network is used to generate a frontal face image that can consider both recognizability and perceptual quality. Quantitative and qualitative evaluations on controlled and in-the-wild databases show that our method outperforms the state-of-the-art.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Babnik, Ž., Damer, N., Štruc, V.: Optimization-based improvement of face image quality assessment techniques. In: 2023 11th International Workshop on Biometrics and Forensics (IWBF), IEEE, pp 1–6 (2023)

  2. Bhattacharya, S., Kyal, C., Routray, A.: Simplified face quality assessment (sfqa). pp 108–114 (2021). https://doi.org/10.1016/j.patrec.2021.03.037, https://www.sciencedirect.com/science/article/pii/S0167865521001331

  3. Boutros, F., Fang, M., Klemt, M., et al.: CR-FIQA: face image quality assessment by learning sample relative classifiability. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 5836–5845 (2023). https://doi.org/10.48550/arXiv.2112.06592

  4. Cao, J., Hu, Y., Zhang, H., et al.: Learning a high fidelity pose invariant model for high-resolution face frontalization. In: Advances In Neural Information Processing Systems, pp 1–11 (2018). https://doi.org/10.48550/arXiv.1806.08472

  5. Chen, K., Yi, T., Lv, Q.: Lightqnet: Lightweight deep face quality assessment for risk-controlled face recognition. IEEE 28, 1878–1882 (2021). https://doi.org/10.1109/LSP.2021.3109781

    Article  CAS  Google Scholar 

  6. Chen, Z., Yang, H.: L2rt-fiqa: Face image quality assessment via learning-to-rank transformer. In: Zhai, G., Zhou, J., Yang, H., et al. (eds.) Digital Multimedia Communications, pp. 270–285. Springer Nature Singapore, Singapore (2023)

  7. Duan, X., Liu, H., Liang, J.: DIQA-FF: Dual image quality assessment for face frontalization. In: Multimedia Tools and Applications. Springer, pp 1–20 (2023). https://doi.org/10.1007/s11042-023-15084-8

  8. Guo, J., Zhu, X., Lei, Z., et al.: Face synthesis for eyeglass-robust face recognition (2018)

  9. Hassner, T., Harel, S., Paz, E., et al.: Effective face frontalization in unconstrained images. IEEE (2014). https://doi.org/10.1109/CVPR.2015.7299058

    Article  Google Scholar 

  10. He, H., Liang, J., Hou, Z., et al.: Multi-pose face reconstruction and gabor-based dictionary learning for face recognition. In: Applied Intelligence. Springer, pp 1–15 (2022). https://doi.org/10.1007/s10489-022-04336-z

  11. He, H., Liang, J., Hou, Z., et al.: Realistic feature perception for face frontalization with dual-mode face transformation. Elsevier, p 121344 (2023). https://doi.org/10.1016/j.eswa.2023.121344

  12. Hernandez-Ortega, J., Galbally, J., Fierrez, J., et al.: Faceqnet: Quality assessment for face recognition based on deep learning. IEEE, pp 1–8 (2019). https://doi.org/10.48550/arxiv.1904.01740

  13. Hernandez-Ortega, J., Fierrez, J., Serna, I., et al.: FaceQgen: Semi-supervised deep learning for face image quality assessment. In: 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021), IEEE, pp 1–8 (2021). https://doi.org/10.48550/arXiv.2201.00770

  14. Hu, Y., Wu, X., Yu, B., et al.: Pose-guided photorealistic face rotation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 8398–8406 (2018)

  15. Huang, G.B., Mattar, M., Berg, T., et al.: 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, pp 1–14 (2008)

  16. Huang, R., Zhang, S., Li, T., et al.: Beyond face rotation: Global and local perception gan for photorealistic and identity preserving frontal view synthesis. In: Proceedings of the IEEE International Conference On Computer Vision, pp 2439–2448 (2017). https://doi.org/10.1109/ICCV.2017.267

  17. Huang, Y.S., Alhlffee, M.H.: Improving face recognition by integrating decision forest into gan, vol. 37, p. 2175108. Taylor & Francis (2023). https://doi.org/10.1080/08839514

  18. Ju, Y.J., Lee, G.H., Hong, J.H., et al.: Complete Face Recovery Gan: Unsupervised joint face rotation and de-occlusion from a single-view image. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp 3711–3721 (2022). https://doi.org/10.5281/zenodo.7416667

  19. Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 1–12 (2019). https://doi.org/10.48550/arXiv.1812.04948

  20. Kavitha, M., RajivKannan, A.: Hybrid convolutional neural network and long short-term memory approach for facial expression recognition (2023)

  21. Kwak, J.G., Li, Y., Yoon, D., et al.: Generate and edit your own character in a canonical view. In: arXiv preprint, pp 1–5 (2022). https://doi.org/10.48550/arXiv.2205.02974

  22. Li, S., Deng, W.: Reliable crowdsourcing and deep locality-preserving learning for unconstrained facial expression recognition. In: IEEE Transactions on Image Processing, vol 28. IEEE, pp 356–370 (2019). https://doi.org/10.1109/TIP.2018.2868382

  23. Li, X., Zhang, S., Hu, J., et al.: image-to-image translation via hierarchical style disentanglement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 8639–8648 (2021). https://doi.org/10.48550/arXiv.2103.01456

  24. Lin, X., Zheng, H., Zhao, P., et al.: Sd-hrnet: Slimming and distilling high-resolution network for efficient face alignment. https://doi.org/10.3390/s23031532 (2023). https://www.mdpi.com/1424-8220/23/3/1532

  25. Liu, Z., Luo, P., Wang, X., et al.: Deep learning face attributes in the wild. In: Proceedings of the IEEE International Conference On Computer Vision, pp 3730–3738 (2015). https://doi.org/10.1109/ICCV.2015.425

  26. Lyu, J., Wang, Z., Xu, F.: Portrait eyeglasses and shadow removal by leveraging 3d synthetic data. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 3429–3439 (2022). https://doi.org/10.48550/arXiv.2203.10474

  27. Ma, N., Zhang, X., Zheng, H.T., et al.: Shufflenet v2: Practical guidelines for efficient cnn architecture design. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 116–131 (2018). https://doi.org/10.48550/arXiv.1807.11164

  28. Maze, B., Adams, J.C., Duncan, J.A., et al.: Iarpa janus benchmark - c: Face dataset and protocol. pp 158–165 (2018). https://api.semanticscholar.org/CorpusID:28375094

  29. Meng, Q., Zhao, S., Huang, Z., et al.: Magface: A universal representation for face recognition and quality assessment. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 14,225–14,234 (2021). https://doi.org/10.48550/arXiv.2103.06627

  30. Ou, F.Z., Chen, X., Zhang, R., et al.: SDD-FIQA: unsupervised face image quality assessment with similarity distribution distance. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 7670–7679 (2021). https://doi.org/10.48550/arXiv.2103.05977

  31. Schlett, T., Rathgeb, C., Henniger, O., et al.: Face image quality assessment: A literature survey, vol. 54, pp. 1–49. ACM New York, NY (2022). https://doi.org/10.1145/3507901

  32. Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 815–823 (2015). https://doi.org/10.1109/CVPR.2015.7298682

  33. Senguptam, S., Chenm, J.C., Castillom, C., et al.: Frontal to profile face verification in the wild. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp 1–9 (2016). https://doi.org/10.1109/WACV.2016.7477558

  34. Song, L., Gong, D., Li, Z., et al.: Occlusion robust face recognition based on mask learning with pairwise differential siamese network. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 773–782 (2019). https://doi.org/10.48550/arXiv.1908.06290

  35. Terhorst, P., Kolf, J.N., Damer, N., et al.: SER-FIQ: Unsupervised estimation of face image quality based on stochastic embedding robustness. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 5651–5660 (2020). https://doi.org/10.1109/CVPR42600.2020.00569

  36. Terhörst, P., Huber, M., Damer, N., et al.: Pixel-level face image quality assessment for explainable face recognition. In: IEEE Transactions on Biometrics, Behavior, and Identity Science. IEEE, pp 1–18 (2023). https://doi.org/10.48550/arxiv.2110.11001

  37. Tian, Y., Peng, X., Zhao, L., et al.: CR-GAN: learning complete representations for multi-view generation. In: arXiv preprint arXiv:1806.11191, pp 1–7 (2018). https://doi.org/10.48550/arXiv.1806.11191

  38. Torbunov, D., Huang, Y., Yu, H., et al.: Uvcgan: Unet vision transformer cycle-consistent gan for unpaired image-to-image translation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp 702–712 (2023). https://doi.org/10.48550/arXiv.2203.02557

  39. Tran, L., Yin, X., Liu, X.: Disentangled representation learning gan for pose-invariant face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 1415–1424 (2017). https://doi.org/10.1109/CVPR.2017.141

  40. Wang, H., Yang, X.: Efficient practices for profile-to-frontal face synthesis and recognition. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 (2023)

  41. Wang, H., Chi, J., Wu, C., et al.: Cross-view information interaction and feedback network for face hallucination. In: booktitle of Visual Communication and Image Representation. Elsevier, pp 103,758–103,768 (2023)

  42. Wei, Y., Liu, M., Wang, H., et al.: Learning flow-based feature warping for face frontalization with illumination inconsistent supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), Springer, pp 558–574 (2020). https://doi.org/10.48550/arXiv.2008.06843

  43. Yin, X., Yu, X., Sohn, K., et al.: Towards large-pose face frontalization in the wild. In: Proceedings of the IEEE International Conference On Computer Vision, pp 3990–3999 (2017). https://doi.org/10.48550/arXiv.1704.06244

  44. Zhang, L., Zhang, L., Bovik, A.C.: A feature-enriched completely blind image uality evaluator. In: IEEE Transactions on Image Processing, vol 24. IEEE, pp 2579–2591 (2015). https://doi.org/10.1109/TIP.2015.2426416

  45. Zhang, Z., Chen, X., Wang, B., et al.: Face frontalization using an appearance-flow-based convolutional neural network. In: IEEE Transactions on Image Processing, pp 2187–2199 (2018). https://doi.org/10.1109/TIP.2018.2883554

  46. Zhao, J., Cheng, Y., Xu, Y., et al.: Towards pose invariant face recognition in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 2207–2216 (2018). https://doi.org/10.1109/CVPR.2018.00235

  47. Zhou, H., Liu, J., Liu, Z., et al.: Rotate-and-render: Unsupervised photorealistic face rotation from single-view images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 5911–5920 (2020). https://doi.org/10.48550/arXiv.2003.08124

  48. Zhu, X., Lei, Z., Yan, J., et al.: High-fidelity pose and expression normalization for face recognition in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 787–796 (2015). https://doi.org/10.1109/CVPR.2015.7298679

Download references

Funding

The research leading to these results received funding from the Basic Science (Natural Science) Research Projects of Universities in Jiangsu Province under Grant Agreement No[22KJB520011].

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by XD. HL and JL provided supervision. The first draft of the manuscript was written by XD, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acquisition of the financial support for the project leading to this publication.

Corresponding author

Correspondence to Hao Liu.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethics approval

This article does not contain any studies with animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, H., Duan, X. & Liang, J. FF-PPQA: Face frontalization without glasses based on perceptual quality and pixel-level quality assessment. SIViP 18, 2879–2893 (2024). https://doi.org/10.1007/s11760-023-02957-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-023-02957-6

Keywords

Navigation