The Development and Challenges of Face Alignment: A survey

  • Athman Dhiya Abdulsatar Department of Mathematics, faculty of Computer Science and Information Technology, University of Al-Qadisiyah, Iraq.
  • Ali Mohsin Aljuboori Department of Mathematics, faculty of Computer Science and Information Technology, University of Al-Qadisiyah, Iraq.
Keywords: Face alignment, Facial landmarks, computer vision


Finding the face key-points location or which is also called Face alignment has gotten a lot of interest in the last twenty years because of its wide range of applications in the major of automatic face analysis. Nonetheless, this task has proven an extremely challenging task in an unconstrained environment because of multiple confusing factors, such as occlusions, position, illumination, and expression. However, this task is still an open problem, even though researchers still developing a great many techniques to face the challenges of this problem, in this survey, a critical review of the current literature in the face alignment field had already been presented focusing on those methods tackling the total of this topic's challenges and difficulties under uncontrollable circumstances. Especially, the existing face alignment techniques have been categorized existing referring to more than the type of categorizing. Then represent a survey of the recent related works in face alignment and lastly, we discuss the general steps of the face alignment system.


Download data is not yet available.


1. Gogić, I., J. Ahlberg, and I.S. Pandžić, Regression-based methods for face alignment: A survey. Signal Processing, 2021. 178: p. 107755.
2. MuratTaskiran, N., Cigdem Eroglu Erdem, Face recognition: Past, present and future (a review). 2020.
3. Kang, S., et al. B-Face: 0.2 mW CNN-based face recognition processor with face alignment for mobile user identification. in 2018 IEEE symposium on VLSI circuits. 2018. IEEE.
4. Sharma, R. and M. Patterh, Face recognition using face alignment and PCA techniques: a literature survey. IOSR Journal of Computer Engineering (IOSR-JCE), 2015. 17(4): p. 17-30.
5. Angulu, R., J.R. Tapamo, and A.O. Adewumi, Age estimation via face images: a survey. EURASIP Journal on Image and Video Processing, 2018. 2018(1): p. 1-35.
6. Treepong, B., H. Mitake, and S. Hasegawa, Makeup creativity enhancement with an augmented reality face makeup system. Computers in Entertainment (CIE), 2018. 16(4): p. 1-17.
7. Jin, X. and X. Tan, Face alignment in-the-wild: A survey. Computer Vision and Image Understanding, 2017. 162: p. 1-22.
8. Zheng, Z., et al. Joint discriminative and generative learning for person re-identification. in proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.
9. Xiaoming Liu, M., IEEE, Discriminative Face Alignment. IEEE, 2009: p. 1947, 1948.
10. Wu, Y. and Q. Ji, Facial landmark detection: A literature survey. International Journal of Computer Vision, 2019. 127(2): p. 115-142.
11. Wang, K. and G. Zhao. A Survey for Traditional, Cascaded Regression, and Deep Learning-Based Face Alignment. in International Conference on Medical Imaging and Computer-Aided Diagnosis. 2020. Springer.
12. Huang, J. and A. Tamrakar, Ace-net: Fine-level face alignment through anchors and contours estimation. arXiv preprint arXiv:2012.01461, 2020.
13. Perrot, R., P. Bourdon, and D. Helbert, Implementing cascaded regression tree-based face landmarking: an in-depth overview. Image and Vision Computing, 2020. 102: p. 103976.
14. Tu, X., et al., 3d face reconstruction from a single image assisted by 2d face images in the wild. IEEE Transactions on Multimedia, 2020. 23: p. 1160-1172.
15. Kumar, A., et al. LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood. in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.
16. Dapogny, A., K. Bailly, and M. Cord. Deep Entwined Learning Head Pose and Face Alignment Inside an Attentional Cascade with Doubly-Conditional fusion. in 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020). 2020. IEEE.
17. Ding, C., et al., Multi-template supervised descent method for face alignment. Cognitive Systems Research, 2021. 65: p. 107-117.
18. Zhen, X., et al., Heterogenous output regression network for direct face alignment. Pattern Recognition, 2020. 105: p. 107311.
19. Wan, J., et al., Robust face alignment by multi-order high-precision hourglass network. IEEE Transactions on Image Processing, 2020. 30: p. 121-133.
20. Lin, C., et al., Structure-Coherent Deep Feature Learning for Robust Face Alignment. IEEE Transactions on Image Processing, 2021. 30: p. 5313-5326.
21. Mo, H., et al., A 460 GOPS/W Improved Mnemonic Descent Method-Based Hardwired Accelerator for Face Alignment. IEEE Transactions on Multimedia, 2020. 23: p. 1122-1135.
22. Park, H. and D. Kim, ACN: Occlusion-tolerant face alignment by attentional combination of heterogeneous regression networks. Pattern Recognition, 2021. 114: p. 107761.
23. Lisha Chen, H.S., Qiang Ji, Face Alignment with Kernel Density Deep Neural Network. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019: p. 6996, 6997.
24. Fengyi Song , X., SongcanChen, Zhi-HuaZhou, A literaturesurveyonrobustandefficient eyelocalization in real-lifescenarios. Elsevier, 2013: p. 3164, 3165.
25. Wu, W., et al. Look at boundary: A boundary-aware face alignment algorithm. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
26. Fard, A.P. and M.H. Mahoor, Facial landmark points detection using knowledge distillation-based neural networks. Computer Vision and Image Understanding, 2022. 215: p. 103316
How to Cite
Abdulsatar, A., & Aljuboori, A. (2022). The Development and Challenges of Face Alignment: A survey. Journal of Al-Qadisiyah for Computer Science and Mathematics, 14(4), Comp Page 82-91.
Computer article