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International Journal of Computer Vision

, Volume 127, Issue 2, pp 115–142 | Cite as

Facial Landmark Detection: A Literature Survey

  • Yue Wu
  • Qiang JiEmail author
Article

Abstract

The locations of the fiducial facial landmark points around facial components and facial contour capture the rigid and non-rigid facial deformations due to head movements and facial expressions. They are hence important for various facial analysis tasks. Many facial landmark detection algorithms have been developed to automatically detect those key points over the years, and in this paper, we perform an extensive review of them. We classify the facial landmark detection algorithms into three major categories: holistic methods, Constrained Local Model (CLM) methods, and the regression-based methods. They differ in the ways to utilize the facial appearance and shape information. The holistic methods explicitly build models to represent the global facial appearance and shape information. The CLMs explicitly leverage the global shape model but build the local appearance models. The regression based methods implicitly capture facial shape and appearance information. For algorithms within each category, we discuss their underlying theories as well as their differences. We also compare their performances on both controlled and in the wild benchmark datasets, under varying facial expressions, head poses, and occlusion. Based on the evaluations, we point out their respective strengths and weaknesses. There is also a separate section to review the latest deep learning based algorithms. The survey also includes a listing of the benchmark databases and existing software. Finally, we identify future research directions, including combining methods in different categories to leverage their respective strengths to solve landmark detection “in-the-wild”.

Keywords

Facial landmark detection Face alignment Survey 

References

  1. Ahlberg, J. (2002). An active model for facial feature tracking. EURASIP Journal on Advances in Signal Processing, 2002(6), 569,028.CrossRefGoogle Scholar
  2. Alabort-I-Medina, J., & Zafeiriou, S. (2014). Bayesian active appearance models. In IEEE conference on computer vision and pattern recognition.Google Scholar
  3. Asthana, A., Zafeiriou, S., Cheng, S., & Pantic, M. (2013). Robust discriminative response map fitting with constrained local models. In IEEE conference on computer vision and pattern recognition, CVPR ’13, pp. 3444–3451.Google Scholar
  4. Asthana, A., Zafeiriou, S., Cheng, S., & Pantic, M. (2014). Incremental face alignment in the wild. In IEEE conference on computer vision and pattern recognition, pp. 1859–1866.Google Scholar
  5. Baker, S., Gross, R., & Matthews, I. (2002). Lucas-kanade 20 years on: A unifying framework: Part 3. International Journal of Computer Vision, 56, 221–255.CrossRefGoogle Scholar
  6. Baltrusaitis, T., Robinson, P., & Morency, L. P. (2014). Continuous conditional neural fields for structured regression. In European conference on computer vision (pp. 593–608). Springer.Google Scholar
  7. Baltrušaitis, T., Robinson, P., & Morency, L. P. (2012). 3D constrained local model for rigid and non-rigid facial tracking. In IEEE conference on computer vision and pattern recognition.Google Scholar
  8. Belhumeur, P., Jacobs, D., Kriegman, D., & Kumar, N. (2013). Localizing parts of faces using a consensus of exemplars. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(12), 2930–2940.CrossRefGoogle Scholar
  9. Belhumeur, P. N., Jacobs, D. W., Kriegman, D. J., & Kumar, N. (2011). Localizing parts of faces using a consensus of exemplars. In IEEE conference on computer vision and pattern recognition.Google Scholar
  10. BioID. https://www.bioid.com/About/BioID-Face-Database. Accessed 30 August 2015.
  11. Bourel, F., Chibelushi, C., & Low, A. (2000). Robust facial feature tracking. In British Machine Vision Conference, pp. 24.1–24.10.Google Scholar
  12. Burgos-Artizzu, X. P., Perona, P., & Dollar, P. (2013). Robust face landmark estimation under occlusion. In IEEE international conference on computer vision, pp. 1513–1520.Google Scholar
  13. Cao, X., Wei, Y., Wen, F., & Sun, J. (2014). Face alignment by explicit shape regression. International Journal of Computer Vision, 107, 177–190.MathSciNetCrossRefGoogle Scholar
  14. Chen, D., Ren, S., Wei, Y., Cao, X., & Sun, J. (2014). Joint cascade face detection and alignment. In D. Fleet, T. Pajdla, B. Schiele, & T. Tuytelaars (Eds.), European Conference on Computer Vision, Lecture Notes in Computer Science (Vol. 8694, pp. 109–122). Berlin: Springer.Google Scholar
  15. Chrysos, G. G., Antonakos, E., Snape, P., Asthana, A., & Zafeiriou, S. (2017). A comprehensive performance evaluation of deformable face tracking "in-the-wild". International Journal of Computer Vision, 126, 198–232.MathSciNetCrossRefGoogle Scholar
  16. Cootes, T., Walker, K., & Taylor, C. (2000). View-based active appearance models. In IEEE international conference on automatic face and gesture recognition, pp. 227–232.Google Scholar
  17. Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001). Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6), 681–685.CrossRefGoogle Scholar
  18. Cootes, T. F., Ionita, M. C., Lindner, C., & Sauer, P. (2012). Robust and accurate shape model fitting using random forest regression voting. In European Conference on Computer Vision—Volume Part VII, pp. 278–291.Google Scholar
  19. Cootes, T. F., Taylor, C. J., Cooper, D. H., & Graham, J. (1995). Active shape models their training and application. Computer Vision and Image Understanding, 61(1), 38–59.CrossRefGoogle Scholar
  20. Cosar, S., & Cetin, M. (2011). A graphical model based solution to the facial feature point tracking problem. Image and Vision Computing, 29(5), 335–350.CrossRefGoogle Scholar
  21. Cristinacce, D., & Cootes, T. (2007). Boosted regression active shape models. In British Machine Vision Conference, pp. 880–889.Google Scholar
  22. Cristinacce, D., & Cootes, T. F. (2004). A comparison of shape constrained facial feature detectors. In International conference on automatic face and gesture recognition, pp. 375–380.Google Scholar
  23. Cristinacce, D., & Cootes, T. F. (2006). Feature detection and tracking with constrained local models. In British Machine Vision Conference.Google Scholar
  24. Dantone, M., Gall, J., Fanelli, G., & Gool, L. V. (2012). Real-time facial feature detection using conditional regression forests. In IEEE conference on computer vision and pattern recognition.Google Scholar
  25. Donner, R., Reiter, M., Langs, G., Peloschek, P., & Bischof, H. (2006). Fast active appearance model search using canonical correlation analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(10), 1690–1694.CrossRefGoogle Scholar
  26. Edwards, G. J., Taylor, C. J., & Cootes, T. F. (1998). Interpreting face images using active appearance models. In IEEE international conference on face and gesture recognition (pp. 300–305). IEEE Computer Society.Google Scholar
  27. Fan, H., & Zhou, E. (2016). Approaching human level facial landmark localization by deep learning. Image and Vision Computing, 47(C), 27–35.CrossRefGoogle Scholar
  28. Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010). Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intellgence, 32(9), 1627–1645.CrossRefGoogle Scholar
  29. Feng, Z. H., Huber, P., Kittler, J., Christmas, W., & Wu, X. J. (2015). Random cascaded-regression copse for robust facial landmark detection. IEEE Signal Processing Letters, 22(1), 76–80.CrossRefGoogle Scholar
  30. Georghiades, A., Belhumeur, P., & Kriegman, D. (2001). From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6), 643–660.CrossRefGoogle Scholar
  31. Ghiasi, G., & Fowlkes, C. (2014). Occlusion coherence: Localizing occluded faces with a hierarchical deformable part model. In IEEE conference on computer vision and pattern recognition, pp. 1899–1906.Google Scholar
  32. Girshick, R. (2015). Fast r-cnn. In The IEEE international conference on computer vision (ICCV).Google Scholar
  33. Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In The IEEE conference on computer vision and pattern recognition (CVPR).Google Scholar
  34. Gou, C., Wu, Y., Wang, F. Y., & Ji, Q. (2016). Shape augmented regression for 3D face alignment, pp. 604–615. Cham.Google Scholar
  35. Gower, J. C. (1975). Generalized procrustes analysis. Psychometrika, 40(1), 33–51.MathSciNetCrossRefzbMATHGoogle Scholar
  36. Gross, R., Matthews, I., & Baker, S. (2004). Appearance-based face recognition and light-fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(4), 449–465.CrossRefGoogle Scholar
  37. Gross, R., Matthews, I., & Baker, S. (2005). Generic vs. person specific active appearance models. Image Vision and Computing, 23(12), 1080–1093.CrossRefGoogle Scholar
  38. Gross, R., Matthews, I., Cohn, J., Kanade, T., & Baker, S. (2010). Multi-pie. Image Vision and Computing, 28(5), 807–813.CrossRefGoogle Scholar
  39. Gu, L., & Kanade, T. (2008). A generative shape regularization model for robust face alignment. In European Conference on Computer Vision: Part I (pp. 413–426). Berlin, Heidelberg: Springer.Google Scholar
  40. Hansen, D. W., & Ji, Q. (2010). In the eye of the beholder: A survey of models for eyes and gaze. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(3), 478–500.CrossRefGoogle Scholar
  41. Heisele, B., Serre, T., & Poggio, T. (2007). A component-based framework for face detection and identification. International Journal of Computer Vision, 74(2), 167–181.CrossRefGoogle Scholar
  42. Hou, X., Li, S., Zhang, H., & Cheng, Q. (2001). Direct appearance models. In IEEE conference on computer vision and pattern recognition, Vol. 1.Google Scholar
  43. Hsu, G. S., Chang, K. H., & Huang, S. C. (2015). Regressive tree structured model for facial landmark localization. In IEEE International conference on computer vision, pp. 3855–3861.Google Scholar
  44. Hu, C., Feris, R., & Turk, M. (2003). Real-time view-based face alignment using active wavelet networks. In IEEE international workshop on analysis and modeling of faces and gestures, pp. 215–221.Google Scholar
  45. Jeni, L. A., Cohn, J. F., & Kanade, T. (2015). Dense 3D face alignment from 2D videos in real-time. In 2015 11th IEEE international conference and workshops on automatic face and gesture recognition (FG). articles/Jeni15FG_ZFace.pdf.Google Scholar
  46. Jiao, F., Li, S., Shum, H., & Schuurmans, D. (2003). Face alignment using statistical models and wavelet features. In IEEE conference on computer vision and pattern recognition.Google Scholar
  47. Jones, M., & Poggio, T. (1998). Multidimensional morphable models: A framework for representing and matching object classes. International Journal of Computer Vision, 29(2), 107–131.CrossRefGoogle Scholar
  48. Jourabloo, A., & Liu, X. (2015). Pose-invariant 3D face alignment. In 2015 IEEE international conference on computer vision (ICCV), pp. 3694–3702.Google Scholar
  49. Jourabloo, A., & Liu, X. (2016). Large-pose face alignment via CNN-based dense 3D model fitting. In IEEE conference on computer vision and pattern recognition. Las Vegas, NV.Google Scholar
  50. Kanade, T., Cohn, J. F., & Tian, Y. Comprehensive database for facial expression analysis. In IEEE international conference on automatic face and gesture recognition, pp. 46–53.Google Scholar
  51. Kazemi, V., & Sullivan, J. (2014). One millisecond face alignment with an ensemble of regression trees. In IEEE conference on computer vision and pattern recognition (CVPR), pp. 1867–1874.Google Scholar
  52. Koestinger, M., Wohlhart, P., Roth, P. M., & Bischof, H. (2011). Annotated facial landmarks in the wild: A large-scale, real-world database for facial landmark localization. In First IEEE international workshop on benchmarking facial image analysis technologies.Google Scholar
  53. Le, V., Brandt, J., Lin, Z., Bourdev, L., & Huang, T. S. (2012). Interactive facial feature localization. In European Conference on Computer Vision—Volume Part III, pp. 679–692.Google Scholar
  54. Levi, G., & Hassncer, T. (2015). Age and gender classification using convolutional neural networks. In 2015 IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp. 34–42.Google Scholar
  55. Li, Y., Wang, S., Zhao, Y., & Ji, Q. (2013). Simultaneous facial feature tracking and facial expression recognition. IEEE Transactions on Image Processing, 22(7), 2559–2573.CrossRefGoogle Scholar
  56. Liang S Wu J, Liang, S., Wu, J., Weinberg, S. M., & Shapiro, L. G. (2013). Improved detection of landmarks on 3D human face data. In Annual international conference of the IEEE Engineering in Medicine and Biology Society.Google Scholar
  57. Lopes, A. T., de Aguiar, E., Souza, A. F. D., & Oliveira-Santos, T. (2017). Facial expression recognition with convolutional neural networks: Coping with few data and the training sample order. Pattern Recognition, 61, 610–628.CrossRefGoogle Scholar
  58. Lucey, P., Cohn, J., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I. (2010). The extended Cohn-Kanade dataset (CK+): A complete dataset for action unit and emotion-specified expression. In IEEE conference on computer vision and pattern recognition workshops, pp. 94–101.Google Scholar
  59. Martínez, A., & Benavente, R. (1998). The AR face database.Google Scholar
  60. Martinez, B., Valstar, M. F., Binefa, X., & Pantic, M. (2013). Local evidence aggregation for regression-based facial point detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(5), 1149–1163.CrossRefGoogle Scholar
  61. Mathias, M., Benenson, R., Pedersoli, M., & Van Gool, L. (2014). Face detection without bells and whistles. In European Conference on Computer Vision.Google Scholar
  62. Matthews, I., & Baker, S. (2004). Active appearance models revisited. International Journal of Computer Vision, 60(2), 135–164.CrossRefGoogle Scholar
  63. Messer, K., Matas, J., Kittler, J., & Jonsson, K. (1999). XM2VTSDB: The extended M2VTS database. In International conference on audio and video-based biometric person authentication, pp. 72–77.Google Scholar
  64. Milborrow, S., & Nicolls, F. (2008). Locating facial features with an extended active shape model. In European Conference on Computer Vision: Part IV (pp. 504–513). Berlin, Heidelberg: Springer.Google Scholar
  65. Murphy-Chutorian, E., & Trivedi, M. (2009). Head pose estimation in computer vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(4), 607–626.CrossRefGoogle Scholar
  66. Nickels, K., & Hutchinson, S. (2002). Estimating uncertainty in SSD-based feature tracking. Image and Vision Computing, 20, 47–58.CrossRefGoogle Scholar
  67. Pantic, M., & Rothkrantz, L. J. M. (2000). Automatic analysis of facial expressions: The state of the art. IEEE Transanctions on Pattern Analysis and Machine Intellgence, 22(12), 1424–1445.CrossRefGoogle Scholar
  68. Papazov, C., Marks, T., & Jones, M. (2015). Real-time head pose and facial landmark estimation from depth images using triangular surface patch features. In IEEE conference on computer vision and pattern recognition (CVPR) (pp. 4722–4730). IEEE.Google Scholar
  69. Patacchiola, M., & Cangelosi, A. (2017). Head pose estimation in the wild using convolutional neural networks and adaptive gradient methods. Pattern Recognition, 71, 132–143.CrossRefGoogle Scholar
  70. Patrick Sauer, T. C., & Taylor, C. (2011). Accurate regression procedures for active appearance models. In British Machine Vision Conference.Google Scholar
  71. Perakis, P., Passalis, G., Theoharis, T., & Kakadiaris, I. A. (2013). 3D facial landmark detection under large yaw and expression variations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(7), 1552–1564.CrossRefGoogle Scholar
  72. Phillips, P. J., Flynn, P. J., Scruggs, T., Bowyer, K. W., Chang, J., Hoffman, K., et al. (2005). Overview of the face recognition grand challenge. In IEEE conference on computer vision and pattern recognition, CVPR ’05 (pp. 947–954). Washington, DC: IEEE Computer Society.Google Scholar
  73. Phillips, P. J., Moon, H., Rauss, P., & Rizvi, S. A. (1997). The FERET evaluation methodology for face-recognition algorithms. In IEEE conference on computer vision and pattern recognition, CVPR ’97 (pp. 137–143). Washington, DC: IEEE Computer Society.Google Scholar
  74. Ranjan, R., Patel, V. M., & Chellappa, R. (2016). Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. CoRR arXiv:1603.01249.
  75. Ren, S., Cao, X., Wei, Y., & Sun, J. (2014). Face alignment at 3000 FPS via regressing local binary features. In IEEE conference on computer vision and pattern recognition (CVPR), pp. 1685–1692.Google Scholar
  76. Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In NIPS.Google Scholar
  77. Sagonas, C., Antonakos, E., Tzimiropoulos, G., Zafeiriou, S., & Pantic, M. (2016). 300 faces in-the-wild challenge: Database and results. Image and Vision Computing, 47, 3–18. 300-W, the First Automatic Facial Landmark Detection in-the-Wild Challenge.Google Scholar
  78. Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., & Pantic, M. (2013). 300 faces in-the-wild challenge: The first facial landmark localization challenge. In IEEE international conference on computer vision, 300 Faces in-the-Wild Challenge (300-W). Sydney, Australia.Google Scholar
  79. Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., & Pantic, M. (2013a). A semi-automatic methodology for facial landmark annotation. In 2013 IEEE conference on computer vision and pattern recognition workshops, pp. 896–903.Google Scholar
  80. Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., & Pantic, M. (2013b). A semi-automatic methodology for facial landmark annotation. In IEEE conference on computer vision and pattern recognition workshop. Portland Oregon, USA.Google Scholar
  81. Saragih, J., & Gocke, R. (2009). Learning AAM fitting through simulation. Pattern Recognition, 42(11), 2628–2636.CrossRefzbMATHGoogle Scholar
  82. Saragih, J., & Goecke, R. (2007). A nonlinear discriminative approach to AAM fitting. In International conference on computer vision, pp. 1–8.Google Scholar
  83. Saragih, J. M., Lucey, S., & Cohn, J. F. (2011). Deformable model fitting by regularized landmark mean-shift. International Journal of Computer Vision, 91(2), 200–215.MathSciNetCrossRefzbMATHGoogle Scholar
  84. Schroff, F., Kalenichenko, D., & Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering.Google Scholar
  85. Shen, J., Zafeiriou, S., Chrysos, G. G., Kossaifi, J., Tzimiropoulos, G., & Pantic, M. (2015). The first facial landmark tracking in-the-wild challenge: Benchmark and results. In The IEEE international conference on computer vision (ICCV) workshops.Google Scholar
  86. Shen, X., Lin, Z., Brandt, J., & Wu, Y. (2013). Detecting and aligning faces by image retrieval. In IEEE conference on computer vision and pattern recognition.Google Scholar
  87. Smith, B., Brandt, J., Lin, Z., & Zhang, L. (2014). Nonparametric context modeling of local appearance for pose- and expression-robust facial landmark localization. In IEEE conference on computer vision and pattern recognition, pp. 1741–1748.Google Scholar
  88. Smith, B. M., & Zhang, L. (2014). Collaborative facial landmark localization for transferring annotations across datasets (pp. 78–93). Cham: Springer.Google Scholar
  89. Sun, Y., Liang, D., Wang, X., & Tang, X. (2015). Deepid3: Face recognition with very deep neural networks. CoRR arXiv:1502.00873.
  90. Sun, Y., Wang, X., & Tang, X. (2013). Deep convolutional network cascade for facial point detection. In IEEE conference on computer vision and pattern recognition, pp. 3476–3483.Google Scholar
  91. Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014). Deepface: Closing the gap to human-level performance in face verification.Google Scholar
  92. Tong, Y., Liu, X., Wheeler, F. W., & Tu, P. H. (2012). Semi-supervised facial landmark annotation. Computer Vision and Image Understanding, 116(8), 922–935.CrossRefGoogle Scholar
  93. Tong, Y., Wang, Y., Zhu, Z., & Ji, Q. (2007). Robust facial feature tracking under varying face pose and facial expression. Pattern Recognition, 40(11), 3195–3208.CrossRefzbMATHGoogle Scholar
  94. Tresadern, P., Sauer, P., & Cootes, T. (2010). Additive update predictors in active appearance models. In British Machine Vision Conference (pp. 91.1–91.12). BMVA Press.Google Scholar
  95. Trigeorgis, G., Snape, P., Nicolaou, M. A., Antonakos, E., & Zafeiriou, S. (2016). Mnemonic descent method: A recurrent process applied for end-to-end face alignment. In IEEE conference on computer vision and pattern recognition (CVPR), pp. 4177–4187. Las Vegas, NV, USA.Google Scholar
  96. Tulyakov, S., & Sebe, N. (2015). Regressing a 3D face shape from a single image. In IEEE international conference on computer vision, pp. 3748–3755.Google Scholar
  97. Tzimiropoulos, G., i medina, J. A., Zafeiriou, S., Pantic, M. (2012). Generic active appearance models revisited. In Asian Conference on Computer Vision, pp. 650–663. Daejeon, Korea.Google Scholar
  98. Tzimiropoulos, G., & Pantic, M. Optimization problems for fast aam fitting in-the-wild. In IEEE international conference on computer vision, pp. 593–600.Google Scholar
  99. Tzimiropoulos, G., & Pantic, M. (2014). Gauss-Newton deformable part models for face alignment in-the-wild. In IEEE conference on computer vision and pattern recognition, pp. 1851–1858.Google Scholar
  100. Uřičář, M., Franc, V., & Hlaváč, V. (2012). Detector of facial landmarks learned by the structured output SVM. In International conference on computer vision theory and applications (Vol. 1, pp. 547–556). Portugal.Google Scholar
  101. Valstar, M., Martinez, B., Binefa, V., & Pantic, M. (2010). Facial point detection using boosted regression and graph models. In IEEE conference on computer vision and pattern recognition, pp. 13–18.Google Scholar
  102. Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In IEEE conference on computer vision and pattern recognition, Vol. 1, pp. I-511–I-518.Google Scholar
  103. Williams, O., Blake, A., & Cipolla, R. (2005). Sparse Bayesian learning for efficient visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1292–1304.CrossRefGoogle Scholar
  104. Wu, Y., & Ji, Q. (2015). Discriminative deep face shape model for facial point detection. International Journal of Computer Vision, 113(1), 37–53.MathSciNetCrossRefGoogle Scholar
  105. Wu, Y., & Ji, Q. (2015). Robust facial landmark detection under significant head poses and occlusion. In International conference on computer vision.Google Scholar
  106. Wu, Y., & Ji, Q. (2016). Constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection. In IEEE conference on computer vision and pattern recognition.Google Scholar
  107. Wu, Y., Wang, Z., & Ji, Q. (2013). Facial feature tracking under varying facial expressions and face poses based on restricted Boltzmann machines. In IEEE conference on computer vision and pattern recognition, pp. 3452–3459.Google Scholar
  108. Wu, Y., Wang, Z., & Ji, Q. (2014). A hierarchical probabilistic model for facial feature detection. In IEEE conference on computer vision and pattern recognition, pp. 1781–1788.Google Scholar
  109. Xiong, X., & De la Torre Frade, F. (2013). Supervised descent method and its applications to face alignment. In IEEE international conference on computer vision and pattern recognition.Google Scholar
  110. Xiong, X., & la Torre, F. D. (2015). Global supervised descent method. In IEEE conference on computer vision and pattern recognition, pp. 2664–2673.Google Scholar
  111. Yan, S., Hou, X., Li, S. Z., Zhang, H., & Cheng, Q. (2003). Face alignment using view-based direct appearance models. Special issue on facial image processing, analysis and synthesis. International Journal of Imaging Systems and Technology, 13, 106–112.CrossRefGoogle Scholar
  112. Yang, H., & Patras, I. (2013). Privileged information-based conditional regression forest for facial feature detection. In IEEE international conference and workshops on automatic face and gesture recognition, pp. 1–6.Google Scholar
  113. Yin, L., Chen, X., Sun, Y., Worm, T., & Reale, M. (2008). A high-resolution 3D dynamic facial expression database. FG 2,3,5.Google Scholar
  114. Yu, X., Huang, J., Zhang, S., Yan, W., & Metaxas, D. (2013). Pose free facial landmark fitting via optimized part mixtures and cascaded deformable shape model. In IEEE international conference on computer vision.Google Scholar
  115. Yu, X., Lin, Z., Brandt, J., & Metaxas, D. N. (2014). Consensus of regression for occlusion-robust facial feature localization. In D. Fleet, T. Pajdla, B. Schiele, & T. Tuytelaars (Eds.), European Conference on Computer Vision, Lecture Notes in Computer Science (Vol. 8692, pp. 105–118). Berlin: Springer.Google Scholar
  116. Zhang, C., & Zhang, Z. (2010). A survey of recent advances in face detection. Tech. Rep. MSR-TR-2010-66.Google Scholar
  117. Zhang, J., Shan, S., Kan, M., & Chen, X. (2014). Coarse-to-fine auto-encoder networks (CFAN) for real-time face alignment. In European Conference on Computer Vision, Part II, pp. 1–16.Google Scholar
  118. Zhang, Z., Luo, P., Loy, C., & Tang, X. (2014). Facial landmark detection by deep multi-task learning. In European Conference on Computer Vision, Part II, pp. 94–108.Google Scholar
  119. Zhang, Z., Luo, P., Loy, C. C., & Tang, X. (2016). Learning deep representation for face alignment with auxiliary attributes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(5), 918–930.CrossRefGoogle Scholar
  120. Zhao, X., Kim, T. K., & Luo, W. (2014). Unified face analysis by iterative multi-output random forests. In IEEE conference on computer vision and pattern recognition, pp. 1765–1772.Google Scholar
  121. Zhou, E., Fan, H., Cao, Z., Jiang, Y., & Yin, Q. (2013). Extensive facial landmark localization with coarse-to-fine convolutional network cascade. In IEEE international conference on computer vision workshops, pp. 386–391.Google Scholar
  122. Zhu, S., Li, C., Change Loy, C., & Tang, X. (2015). Face alignment by coarse-to-fine shape searching. In IEEE conference on computer vision and pattern recognition.Google Scholar
  123. Zhu, X., Lei, Z., Liu, X., Shi, H., Li, S. (2016). Face alignment across large poses: A 3D solution. In IEEE conference on computer vision and pattern recognition. Las Vegas, NV.Google Scholar
  124. Zhu, X., & Ramanan, D. (2012). Face detection, pose estimation, and landmark localization in the wild. In IEEE conference on computer vision and pattern recognition, pp. 2879–2886.Google Scholar

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Authors and Affiliations

  1. 1.Department of Electrical, Computer, and Systems EngineeringRensselaer Polytechnic InstituteTroyUSA

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