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Facial Features Detection: A Comparative Study

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Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021) (AICV 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1377))

Abstract

Facial feature detection is an essential step in many human face-related applications including facial expression analysis, 3D face reconstruction, pose normalization, vision-based biometrics, and face recognition/verification. Though facial features detection or fiducial facial landmark points may seem an easy process for humans, it faces some challenges as the selection accuracy varies depending on some factors, such as pose, expression, illumination, and occlusion. Several facial landmark detection methods have been introduced to automatically detect those key points over the last ten years. In this paper, a comparative study is conducted to evaluate the efficiency of the state-of-the-art methods used for facial features detection. Seven fiducial points are considered in this study, namely left eye left corner, left eye right corner, right eye left corner, right eye right corner, left mouth corner, right mouth corner, and the tip of the nose. The experiments are conducted on three widely-adopted challenging datasets: BioID, AFLW, and 300W. The experimental results obtained on these three benchmark datasets show that the robustness can be further improved for detection algorithms and reveal future avenues for further research on the topic.

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References

  1. Wu, Y., Ji, Q.: Facial landmark detection: a literature survey. Int. J. Comput. Vis. 127(2), 115–142 (2019)

    Article  Google Scholar 

  2. Zheng, X., Guo, Y., Huang, H., Li, Y., He, R.: A survey of deep facial attribute analysis. Int. J. Comput. Vis. 1–33 (2020)

    Google Scholar 

  3. Hassaballah, M., Kanazawa, T., Ido, S.: Efficient eye detection method based on grey intensity variance and independent components analysis. IET Comput. Vis. 4(4), 261–271 (2010)

    Article  Google Scholar 

  4. Gao, P., Lu, K., Xue, J., Shao, L., Lyu, J.: A coarse-to-fine facial landmark detection method based on self-attention mechanism. IEEE Trans. Multimed.(2020)

    Google Scholar 

  5. Dong, X., Yan, Y., Ouyang, W., Yang, Y.: Style aggregated network for facial landmark detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 379–388 (2018)

    Google Scholar 

  6. Hassaballah, M., Murakami, K., Ido, S.: An automatic eye detection method for gray intensity facial images. Int. J. Comput. Sci. Issues 8(4), 272 (2011)

    Google Scholar 

  7. Hassaballah, M., Murakami, K., Ido, S.: Eye and nose fields detection from gray scale facial images. In: IAPR Conference on Machine Vision Applications, pp. 406–409 (2011)

    Google Scholar 

  8. Lin, J., Xiao, L., Wu, T.: Face recognition for video surveillance with aligned facial landmarks learning. Technol. Health Care 26(1), 169–178 (2018)

    Article  Google Scholar 

  9. Gang, Z., Yuding, K., Weikang, Z., Hassaballah, M.: Advances and trends in video face alignment. In: Recent Advances in Computer Vision: Theories and Applications, pp. 61–84. Springer (2019)

    Google Scholar 

  10. Queirolo, C.C., Silva, L., Bellon, O.R., Segundo, M.P.: 3D face recognition using simulated annealing and the surface interpenetration measure. IEEE Trans. Pattern Anal. Mach. Intell. 32(2), 206–219 (2009)

    Article  Google Scholar 

  11. Li, D., Wang, Z., Gao, Q., Song, Y., Yu, X., Wang, C.: Facial expression recognition based on electroencephalogram and facial landmark localization. Technol. Health Care 27(4), 373–387 (2019)

    Article  Google Scholar 

  12. Balasubramanian, Y., Chandrasekaran, N., Asokan, S., Subramanian, S.S.: Deep-facial feature-based person reidentification for authentication in surveillance applications. In: Visual Object Tracking in the Deep Neural Networks Era. IntechOpen (2019)

    Google Scholar 

  13. Chen, C., Dantcheva, A., Ross, A.: Automatic facial makeup detection with application in face recognition. In: International Conference on Biometrics, pp. 1–8. IEEE (2013)

    Google Scholar 

  14. Johnston, B., de Chazal, P.: A review of image-based automatic facial landmark identification techniques. EURASIP J. Image Video Process. 2018(86), 1–23 (2018)

    Google Scholar 

  15. Asthana, A., Zafeiriou, S., Cheng, S., Pantic, M.: Robust discriminative response map fitting with constrained local models. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3444–3451 (2013)

    Google Scholar 

  16. Kazemi, V., Sullivan, J.: One millisecond face alignment with an ensemble of regression trees. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1867–1874 (2014)

    Google Scholar 

  17. Zhang, Z., Luo, P., Loy, C.C., Tang, X.: Facial landmark detection by deep multi-task learning. In: European Conference on Computer Vision, pp. 94–108. Springer (2014)

    Google Scholar 

  18. Bulat, A., Tzimiropoulos, G.: How far are we from solving the 2D & 3D face alignment problem? (and a dataset of 230,000 3D facial landmarks). In: IEEE International Conference on Computer Vision, pp. 1021–1030 (2017)

    Google Scholar 

  19. Guo, X., Li, S., Zhang, J., Ma, J., Ma, L., Liu, W., Ling, H.: PFLD: a practical facial landmark detector. ArXiv abs/1902.10859 (2019)

    Google Scholar 

  20. Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: European Conference on Computer Vision, pp. 483–499. Springer (2016)

    Google Scholar 

  21. Bulat, A., Tzimiropoulos, G.: Binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources. In: IEEE International Conference on Computer Vision, pp. 3706–3714 (2017)

    Google Scholar 

  22. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: inverted residuals and linear bottlenecks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)

    Google Scholar 

  23. Hassaballah, M., Bekhet, S., Rashed, A.A., Zhang, G.: Facial features detection and localization. In: Recent Advances in Computer Vision: Theories and Applications, pp. 33–59. Springer (2019)

    Google Scholar 

  24. https://www.bioid.com/facedb/

  25. Koestinger, M., Wohlhart, P., Roth, P.M., Bischof, H.: Annotated facial landmarks in the wild: a large-scale, real-world database for facial landmark localization. In: IEEE International Conference on Computer Vision Workshops, pp. 2144–2151. IEEE (2011)

    Google Scholar 

  26. Sagonas, C., Antonakos, E., Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: 300 faces in-the-wild challenge: database and results. Image Vis. Comput. 47, 3–18 (2016)

    Article  Google Scholar 

  27. Sun, K., Wu, W., Liu, T., Yang, S., Wang, Q., Zhou, Q., Ye, Z., Qian, C.: FAB: a robust facial landmark detection framework for motion-blurred videos. In: IEEE International Conference on Computer Vision, pp. 5462–5471 (2019)

    Google Scholar 

  28. Fan, H., Zhou, E.: Approaching human level facial landmark localization by deep learning. Image Vis. Comput. 47, 27–35 (2016)

    Article  Google Scholar 

  29. Yang, H., Jia, X., Loy, C.C., Robinson, P.: An empirical study of recent face alignment methods. arXiv (2015)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, Egypt

    Eman Salem, Mountasser M. Mahmoud & Abdel-Magid M. Ali

  2. Department of Computer Science, Faculty of Computers and Information, South Valley University, Qena, Egypt

    M. Hassaballah

Authors
  1. Eman Salem
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  2. M. Hassaballah
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  3. Mountasser M. Mahmoud
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  4. Abdel-Magid M. Ali
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Corresponding author

Correspondence to Mountasser M. Mahmoud .

Editor information

Editors and Affiliations

  1. Information Technology Department, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Sciences and Techniques, Hassan 1st University, Settat, Morocco

    Abdelkrim Haqiq

  3. School of Medicine, University of Missouri, Columbia, MO, USA

    Peter J. Tonellato

  4. ISAE-ENSMA, Futuroscope Chasseneuil Cedex, France

    Ladjel Bellatreche

  5. British University Vietnam, Hung Yen, Vietnam

    Sam Goundar

  6. Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

    Ahmad Taher Azar

  7. NEST Research Group, ENSEM, Hassan II University of Casablanca, Casablanca, Morocco

    Essaid Sabir

  8. High National School for Computer Science and Systems Analysis (ENSIAS), Mohammed V University, Rabat, Morocco

    Driss Bouzidi

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© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Salem, E., Hassaballah, M., Mahmoud, M.M., Ali, AM.M. (2021). Facial Features Detection: A Comparative Study. In: Hassanien, A.E., et al. Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021). AICV 2021. Advances in Intelligent Systems and Computing, vol 1377. Springer, Cham. https://doi.org/10.1007/978-3-030-76346-6_37

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