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access icon free Robust facial landmark detection using mixture of discriminative visibility-aware models

© The Institution of Engineering and Technology
Received 27/10/2015, Accepted 08/05/2016, Revised 13/04/2016, Published 09/05/2016

Facial landmark detection is a fundamental process included in many face analysis tasks. However, it faces great challenges for the facial images to be detected usually containing large variations, which will deteriorate the detection precision. Among such variations, partial occlusions and pose variations take great effects. In this study, the authors present a mixture of discriminative visibility-aware models (MDVMs) for facial landmark detection, to improve the generalisation ability of the model to occlusion and pose variation. By adopting different structure constrains for different poses as well as selecting different appearance model for the occluded parts, the MDVMs method can efficiently address the problem of partial occlusion and pose variation. Experiment results demonstrate that their proposed MDVMs method outperforms the well-known template-based methods, and can obtain much more accurate and robust facial landmarks detection results under both occlusions and pose variations.

Inspec keywords: face recognition

Other keywords: MDVM method; face analysis tasks; facial images; facial landmark detection; mixture of discriminative visibility-aware models

Subjects: Computer vision and image processing techniques; Image recognition

References

    1. 1)
      • 19. Tan, X., Song, F., Zhou, Z.-H., et al: ‘Enhanced pictorial structures for precise eye localization under in controlled conditions’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2009, pp. 1621–1628.
    2. 2)
      • 30. Gross, R., Matthews, I., Cohn, J., et al: ‘Multi-PIE’. Eighth IEEE Int. Conf. on Automatic Face & Gesture Recognition (FG), 2008, pp. 1–8.
    3. 3)
      • 11. Vukadinovic, D., Pantic, M.: ‘Fully automatic facial feature point detection using Gabor feature based boosted classifiers’. IEEE Int. Conf. on Systems, Man and Cybernetics, 2005, vol. 2, pp. 1692–1698.
    4. 4)
      • 1. Martinez, B., Valstar, M., Binefa, X., et al: ‘Local evidence aggregation for regression based facial point detection’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 35, pp. 2–16.
    5. 5)
      • 9. Liu, Y., Zhou, X., Li, Y., et al: ‘Visibility-aware part model for robust facial point detection’, Neurocomputing, 2015, 149, (Part C), pp. 1515–1523.
    6. 6)
      • 27. Azizpour, H., Laptev, I.: ‘Object detection using strongly-supervised deformable part models’. Computer Vision-ECCV 2012, 2012, pp. 836–849.
    7. 7)
      • 14. Cristinacce, D., Cootes, T.: ‘Boosted regression active shape models’. Proc. British Machine Vision Conf., 2007, pp. 880–889.
    8. 8)
      • 7. Zhu, X., Ramanan, D.: ‘Face detection, pose estimation, and landmark localization in the wild’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2012, pp. 2879–2886.
    9. 9)
      • 18. Yang, Y., Ramanan, D.: ‘Articulated human detection with flexible mixtures-of-parts’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 35, pp. 1–15.
    10. 10)
      • 25. Uricar, M., Franc, V., Thomas, D., et al: ‘Real-time multi-view facial landmark detector learned by the structured output SVM’. 11th IEEE Int. Conf. and Workshops on Automatic Face and Gesture Recognition (FG), 2015, pp. 1–8.
    11. 11)
      • 22. Burgos-Artizzu, X.P., Perona, P., Dollar, P.: ‘Robust face landmark estimation under occlusion’. IEEE Int. Conf. on Computer Vision (ICCV), 2013, pp. 1513–1520.
    12. 12)
      • 16. Zhang, Z., Luo, P., Loy, C.C., et al: ‘Facial landmark detection by deep multi-task learning’. Computer Vision ECCV 2014, 2014, pp. 94–108.
    13. 13)
      • 33. Asthana, A., Zafeiriou, S., Cheng, S., et al: ‘Incremental face alignment in the wild’. 2014 IEEE Conf. on Computer Vision and Pattern Recognition, 2014, pp. 1859–1866.
    14. 14)
      • 2. Shan, S., Chang, Y., Gao, W., et al: ‘Curse of mis-alignment in face recognition: problem and a novel mis-alignment learning solution’. Sixth IEEE Int. Conf. on Automatic Face and Gesture Recognition, 2004, pp. 314–320.
    15. 15)
      • 32. Rapp, V., Senechal, T., Bailly, K., et al: ‘Multiple kernel learning SVM and statistical validation for facial landmark detection’. IEEE Int. Conf. on Automatic Face & Gesture Recognition and Workshops (FG), 2011, pp. 265–271.
    16. 16)
      • 24. Ghiasi, G., Fowlkes, C.C.: ‘Occlusion coherence: localizing occluded faces with a hierarchical deformable part model’. 2014 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 1899–1906.
    17. 17)
      • 17. Yi, S., Xiaogang, W., Xiaoou, T.: ‘Deep convolutional network cascade for facial point detection’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 3476–3483.
    18. 18)
      • 4. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.: ‘Eigenfaces vs. Fisherfaces: recognition using class specific linear projection’, IEEE Trans. Pattern Anal. Mach. Intell., 1997, 19, pp. 711–720.
    19. 19)
      • 13. Dibeklioglu, H., Salah, A.A., Gevers, T.: ‘A statistical method for 2-d facial landmarking’, IEEE Trans. Image Process., 2012, 21, pp. 844–858.
    20. 20)
      • 31. Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: ‘Robust face detection using the Hausdorff distance’. Audio- and Video-based Biometric Person Authentication, 2001, pp. 90–95.
    21. 21)
      • 28. Yang, Y., Ramanan, D.: ‘Articulated pose estimation with flexible mixtures-of-parts’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2011, pp. 1385–1392.
    22. 22)
      • 21. Zhen-Hua, F., Guosheng, H., Kittler, J., et al: ‘Cascaded collaborative regression for robust facial landmark detection trained using a mixture of synthetic and real images with dynamic weighting’, IEEE Trans. Image Process., 2015, 24, pp. 3425–3440.
    23. 23)
      • 26. Dalal, N., Triggs, B.: ‘Histograms of oriented gradients for human detection’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR), 2005, pp. 886–893.
    24. 24)
      • 20. Xiang, Y., Junzhou, H., Shaoting, Z., et al: ‘Pose-free facial landmark fitting via optimized part mixtures and cascaded deformable shape model’. 2013 IEEE Int. Conf. on Computer Vision (ICCV), 2013, pp. 1944–1951.
    25. 25)
      • 5. Milborrow, S., Nicolls, F.: ‘Locating facial features with an extended active shape model’. IEEE European Conf. on Computer Vision (ECCV), 2008, pp. 504–513.
    26. 26)
      • 34. Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., et al: ‘300 faces in-the-wild challenge: the first facial landmark localization challenge’. 2013 IEEE Int. Conf. on Computer Vision Workshops (ICCVW), 2013, pp. 397–403.
    27. 27)
      • 6. Cristinacce, D., Cootes, T.: ‘Automatic feature localisation with constrained local models’, Pattern Recognit., 2008, 41, pp. 3054–3067.
    28. 28)
      • 8. Felzenszwalb, P.F., Huttenlocher, D.P.: ‘Pictorial structures for object recognition’, Int. J. Comput. Vis., 2005, 61, pp. 55–79.
    29. 29)
      • 15. Belhumeur, P., Jacobs, D., Kriegman, D., et al: ‘Localizing parts of faces using a consensus of exemplars’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, PP, pp. 1–11.
    30. 30)
      • 23. Zhen-Hua, F., Huber, P., Kittler, J., et al: ‘Random cascaded-regression copse for robust facial landmark detection’, IEEE Signal Process. Lett., 2015, 22, pp. 76–80.
    31. 31)
      • 29. Huang, G.B., Mattar, M., Berg, T., et al: ‘Labeled faces in the wild: a database for studying face recognition in unconstrained environments’. Workshop on Faces in 'Real-Life' Images: Detection, Alignment, and Recognition, 2008.
    32. 32)
      • 3. Turk, M., Pentland, A.: ‘Eigenfaces for recognition’, J. Cogn. Neurosci., 1991, 3, pp. 71–86.
    33. 33)
      • 10. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., et al: ‘Object detection with discriminatively trained part-based models’, IEEE Trans. Pattern Anal. Mach. Intell., 2010, 32, pp. 1627–1645.
    34. 34)
      • 12. Cristinacce, D., Cootes, T.: ‘Feature detection and tracking with constrained local models’. Proc. British Machine Vision Conf., 2006, pp. 929–938.
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