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Facial Features Detection and Localization

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Recent Advances in Computer Vision

Part of the book series: Studies in Computational Intelligence ((SCI,volume 804))

Abstract

Detection of facial landmarks and their feature points plays an important role in many facial image-related applications such as face recognition/verification, facial expression analysis, pose normalization, and 3D face reconstruction. Generally, detection of facial features is easy for persons; however, for machines it is not an easy task at all. The difficulty comes from high inter-personal variation (e.g., gender, race), intra-personal changes (e.g., pose, expression), and from acquisition conditions (e.g., lighting, image resolution). This chapter discusses basic concepts related to the problem of facial landmarks detection and overviews the successes and failures of exiting solutions. Also, it explores the difficulties that hinders the path of progress in the topic and the challenges involved in the adaptation of existing approaches to build successful systems that can be utilized in real-world facial images-related applications. Additionally, it discusses the performance evaluation metrics and the available benchmarking datasets. Finally, it suggests some possible future directions for research in the topic.

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References

  1. Park, C.W., Lee, T.: A robust facial feature detection on mobile robot platform. Mach. Vis. Appl. 21(6), 981–988 (2010)

    Article  Google Scholar 

  2. Zhang, N., Jeong, H.Y.: A retrieval algorithm for specific face images in airport surveillance multimedia videos on cloud computing platform. Multimed. Tools Appl. 76(16), 17129–17143 (2017)

    Article  Google Scholar 

  3. Song, F., Tan, X., Chen, S., Zhou, Z.H.: A literature survey on robust and efficient eye localization in real-life scenarios. Pattern Recognit. 46(12), 3157–3173 (2013)

    Article  Google Scholar 

  4. Valenti, R., Sebe, N., Gevers, T.: What are you looking at? Int. J. Comput. Vis. 98(3), 324–334 (2012)

    Article  MathSciNet  Google Scholar 

  5. Tak\(\acute{\rm {a}}\)cs, B., Wechsler, H.: Detection of faces and facial landmarks using iconic filter banks. Pattern Recognit. 30(10), 1623–1636 (1997)

    Google Scholar 

  6. Segundo, M., Silva, L., Bellon, O., Queirolo, C.: Automatic face segmentation and facial landmark detection in range images. IEEE Trans. Syst. Man Cybern. Part B Cybern. 40(5), 1319–1330 (2010)

    Article  Google Scholar 

  7. Campadelli, P., Lanzarotti, R.: Fiducial point localization in color images of face foregrounds. Image Vis. Comput. 22(11), 863–872 (2004)

    Article  Google Scholar 

  8. Valstar, M., Martinez, B., Binefa, X., Pantic, M.: Facial point detection using boosted regression and graph models. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2729–2736. IEEE(2010)

    Google Scholar 

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

    Google Scholar 

  10. Hassaballah, M., Aly, S.: Face recognition: challenges, achievements and future directions. IET Comput. Vis. 9(4), 614–626 (2015)

    Article  Google Scholar 

  11. Gizatdinova, Y., Surakka, V.: Automatic edge-based localization of facial features from images with complex facial expressions. Pattern Recognit. Lett. 31(15), 2436–2446 (2010)

    Article  Google Scholar 

  12. Hassaballah, M., Kanazawa, T., Ido, S., Ido, S.: Independent components analysis-based nose detection method. In: 3rd IEEE International Congress on Image and Signal Processing (CISP), vol. 4, pp. 1863–1867 (2010)

    Google Scholar 

  13. Panis, G., Lanitis, A., Tsapatsoulis, N., Cootes, T.F.: Overview of research on facial ageing using the FG-NET ageing database. IET Biom. 5(2), 37–46 (2016)

    Article  Google Scholar 

  14. Jung, Y., Kim, D., Son, B., Kim, J.: An eye detection method robust to eyeglasses for mobile iris recognition. Expert Syst. Appl. 67, 178–188 (2017)

    Article  Google Scholar 

  15. Masi, I., Chang, F.J., Choi, J., Harel, S., Kim, J., Kim, K., Leksut, J., Rawls, S., Wu, Y., Hassner, T., et al.: Learning pose-aware models for pose-invariant face recognition in the wild. IEEE Trans. Pattern Anal. Mach. Intell. (2018)

    Google Scholar 

  16. Queirolo, C., Silva, L., Bellon, O., Segundo, M.: 3D face recognition using simulated annealing and the surface interpenetration measure. IEEE Trans. Pattern Anal. Mach. Intell. 32(2), 206–219 (2010)

    Article  Google Scholar 

  17. Zou, J., Ji, Q., Nagy, G.: A comparative study of local matching approach for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 16(10), 2617–2628 (2007)

    Article  MathSciNet  Google Scholar 

  18. Best-Rowden, L., Jain, A.K.: Longitudinal study of automatic face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(1), 148–162 (2018)

    Article  Google Scholar 

  19. Lin, J., Ming, J., Crookes, D.: Robust face recognition with partial occlusion, illumination variation and limited training data by optimal feature selection. IET Comput. Vis. 5(1), 23–32 (2011)

    Article  MathSciNet  Google Scholar 

  20. Arca, S., Campadelli, P., Lanzarotti, R.: A face recognition system based on automatically determined facial fiducial points. Pattern Recognit. 39(3), 432–443 (2006)

    Article  MATH  Google Scholar 

  21. Ortega, D.G., Pernas, F., Zarzuela, M., Rodriguez, M., Higuera, J.D., Giralda, D.: Real-time hands, face and facial features detection and tracking: Application to cognitive rehabilitation tests monitoring. J. Netw. Comput. Appl. 33(4), 447–466 (2010)

    Article  Google Scholar 

  22. Moriyama, T., Kanade, T., Xiao, J., Cohn, J.: Meticulously detailed eye region model and its application to analysis of facial images. IEEE Trans. Pattern Anal. Mach. Intell. 28(5), 738–752 (2006)

    Article  Google Scholar 

  23. Zhang, L., Mistry, K., Jiang, M., Neoh, S.C., Hossain, M.A.: Adaptive facial point detection and emotion recognition for a humanoid robot. Comput. Vis. Image Underst. 140, 93–114 (2015)

    Article  Google Scholar 

  24. Liew, A.C., Leung, S., Lau, W.: Segmentation of color lip images by spatial fuzzy clustering. IEEE Trans. Fuzzy Syst. 11(4), 542–549 (2003)

    Article  Google Scholar 

  25. Li, M., Cheung, Y.M.: Automatic lip localization under face illumination with shadow consideration. Signal Process. 89(12), 2425–2434 (2009)

    Article  MATH  Google Scholar 

  26. Lin, B.S., Yao, Y.H., Liu, C.F., Lien, C.F., Lin, B.S.: Development of novel lip-reading recognition algorithm. IEEE Access 5, 794–801 (2017)

    Article  Google Scholar 

  27. Fanelli, G., Gall, J., Gool, L.V.: Hough transform-based mouth localization for audio-visual speech recognition. In: British Machine Vision Conference (BMVC’09), London, UK, 7–10 Sept 2009

    Google Scholar 

  28. Lu, Y., Yan, J., Gu, K.: Review on automatic lip reading techniques. Int. J. Pattern Recognit. Artif. Intell. 1856007 (2017)

    Google Scholar 

  29. Berretti, S., Werghi, N., Del Bimbo, A., Pala, P.: Matching 3D face scans using interest points and local histogram descriptors. Comput. Graph. 37(5), 509–525 (2013)

    Article  Google Scholar 

  30. Yang, S., Bhanu, B.: Facial expression recognition using emotion avatar image. In: 2011 IEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG 2011), pp. 866–871. IEEE(2011)

    Google Scholar 

  31. Tawari, A., Trivedi, M.M.: Face expression recognition by cross modal data association. IEEE Trans. Multimed. 15(7), 1543–1552 (2013)

    Article  Google Scholar 

  32. Barnes, C., Zhang, F.L.: A survey of the state-of-the-art in patch-based synthesis. Comput. Vis. Media 3(1), 3–20 (2017)

    Article  Google Scholar 

  33. Shu, Z., Shechtman, E., Samaras, D., Hadap, S.: Eyeopener: editing eyes in the wild. ACM Trans. Graph. (TOG) 36(1), 1 (2017)

    Article  Google Scholar 

  34. Bradley, D., Heidrich, W., Popa, T., Sheffer, A.: High resolution passive facial performance capture. ACM Trans. Graph. (TOG). In: Proceedings of ACM SIGGRAPH’10, vol. 29, USA, 25–29 July 2010

    Google Scholar 

  35. Sariyanidi, E., Gunes, H., Cavallaro, A.: Automatic analysis of facial affect: a survey of registration, representation, and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(6), 1113–1133 (2015)

    Article  Google Scholar 

  36. Dong, X., Yan, Y., Ouyang, W., Yang, Y.: Style aggregated network for facial landmark detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 379–388 (2018)

    Google Scholar 

  37. Hassaballah, M., Murakami, K., Ido, S.: Face detection evaluation: a new approach based on the golden ratio \( \phi \). Signal Image Video Process. 7(2), 307–316 (2013)

    Article  Google Scholar 

  38. Kawulok, M., Celebi, E., Smolka, B.: Advances in Face Detection and Facial Image Analysis. Springer (2016)

    Google Scholar 

  39. Hansen, D., Ji, Q.: In the eye of the beholder: a survey of models for eyes and gaze. IEEE Trans. Pattern Anal. Mach. Intell. 32(3), 478–500 (2010)

    Article  Google Scholar 

  40. Feng, G., Yuen, P.: Multi-cues eye detection on gray intensity image. Pattern Recognit. 34(5), 1033–1046 (2001)

    Article  MATH  Google Scholar 

  41. Qiong, W., Yang, J.: Eye detection in facial images with unconstrained background. J. Pattern Recognit. Res. 1(1), 55–62 (2006)

    Article  Google Scholar 

  42. Song, J., Chi, Z., Liu, J.: A robust eye detection method using combined binary edge and intensity information. Pattern Recognit. 39(6), 1110–1125 (2006)

    Article  MATH  Google Scholar 

  43. Wang, J., Yin, L.: Eye detection under unconstrained background by the terrain feature. In: IEEE International Conference on Multimedia & Expo, pp. 1528–1531. Amsterdam, The Netherlands, 6–8 July 2005

    Google Scholar 

  44. Qian, Z., Xu, D.: Automatic eye detection using intensity filtering and k-means clustering. Pattern Recognit. Lett. 31(12), 1633–1640 (2010)

    Article  Google Scholar 

  45. Mohammad, K., Reza, S.: Human eye sclera detection and tracking using a modified time adaptive self-organizing map. Pattern Recognit. 41(8), 2571–2593 (2008)

    Article  Google Scholar 

  46. Yuille, A., Hallinan, P., Cohen, D.: Feature extraction from faces using deformable templates. Int. J. Comput. Vis. 8(2), 99–111 (1992)

    Article  Google Scholar 

  47. Ryu, Y., Oh, S.: Automatic extraction of eye and mouth fields from a face image using eignfeatures and multilayer perceptrons. Pattern Recognit. 34(12), 2459–2466 (2001)

    Article  MATH  Google Scholar 

  48. Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust face detection using the Hausdorff distance. Lecture Notes in Computer Science (LNCS), vol. 2091, pp. 212–227 (2001)

    Google Scholar 

  49. Wang, J.W., Chen, W.Y.: Eye detection based on head contour geometry and wavelet subband projection. Opt. Eng. 45(5), 57001–57013 (2006)

    Article  Google Scholar 

  50. Wu, J., Trivedi, M.: A binary tree for probability learning in eye detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR’05), pp. 170–178, San Diego, CA, USA, 20–26 June 2005

    Google Scholar 

  51. Peng, K., Chen, L., Ruan, S., Kukharevh, G.: A robust algorithm for eye detection on gray intensity face without spectacles. J. Comput. Sci. Technol. (JCS&T) 5(3), 127–132 (2005)

    Google Scholar 

  52. 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 

  53. Jian, M., Lam, K.M., Dong, J.: Facial-feature detection and localization based on a hierarchical scheme. Inf. Sci. 262, 1–14 (2014)

    Article  Google Scholar 

  54. Kroon, B., Maas, S., Boughorbel, S., Hanjalic, A.: Eye localization in low and standard definition content with application to face matching. Comput. Vis. Image Underst. 113(8), 921–933 (2009)

    Article  Google Scholar 

  55. Chen, S., Liu, C.: Eye detection using discriminatory haar features and a new efficient SVM. Image Vis. Comput. 33, 68–77 (2015)

    Article  Google Scholar 

  56. Mark, E., Andrew, Z.: Regression and classification approaches to eye localization in face images. In: 7th International Conference on Automatic Face and Gesture Recognition (FG’06), pp. 441–448, UK, 10–12 Apr 2006

    Google Scholar 

  57. Ian, F., Bret, F., Javier, M.: A generative framework for real time object detection and classification. Comput. Vis. Image Underst. 98(1), 182–210 (2005)

    Article  Google Scholar 

  58. Wang, P., Ji, Q.: Multi-view face and eye detection using discriminant features. Comput. Vis. Image Underst. 105(2), 99–111 (2007)

    Article  Google Scholar 

  59. Wang, P., Green, M., Ji, Q., Wayman, J.: Automatic eye detection and its validation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 164–171, San Diego, CA, USA, 20–26 June 2005

    Google Scholar 

  60. Tang, X., Zongying, O., Tieming, S., Haibo, S., Pengfei, Z.: Robust precise eye location by Adaboost and SVM techniques. Lecture Notes in Computer Science (LNCS), vol. 3497, pp. 93–98 (2005)

    Chapter  Google Scholar 

  61. Karczmarek, P., Pedrycz, W., Reformat, M., Akhoundi, E.: A study in facial regions saliency: a fuzzy measure approach. Soft Comput. 18(2), 379–391 (2014)

    Article  Google Scholar 

  62. Liew, A.W.C., Leung, S.H., Lau, W.H.: Lip contour extraction from color images using a deformable model. IEEE Trans. Image Process. 35(12), 2949–2962 (2002)

    MATH  Google Scholar 

  63. Leung, S.H., Wang, S.L., Lau, W.H.: Lip image segmentation using fuzzy clustering incorporating an elliptic shape function. IEEE Trans. Image Process. 13(1), 51–62 (2004)

    Article  Google Scholar 

  64. Matthews, I., Cootes, T., Bangham, J.: Extraction of visual features for lip reading. IEEE Trans. Pattern Anal. Mach. Intell. 24(2), 198–213 (2002)

    Article  Google Scholar 

  65. Harvey, R., Matthews, I., Bangham, J.A., Cox, S.: Lip reading from scale-space measurements. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 582–587, Puerto Rico, 17–19 June 1997

    Google Scholar 

  66. Nakata, Y., Ando, M.: Lipreading method using color extraction method and eigenspace technique. Syst. Comput. Jpn 35(3), 12–23 (2004)

    Article  Google Scholar 

  67. Lienhart, R., Liang, L., Kuranov, A.: A detector tree of boosted classifiers for real-time object detection and tracking. In: International Conference on Multimedia and Expo (ICME’03), pp. 582–587, Baltimore, MD, USA, 6–9 July 2003

    Google Scholar 

  68. Zuo, F., de With, P.H.: Facial feature extraction by a cascade of model-based algorithms. Signal Process. Image Commun. 23(3), 194–211 (2008)

    Article  Google Scholar 

  69. Ding, L., Martinez, A.M.: Features versus context: an approach for precise and detailed detection and delineation of faces and facial features. IEEE Trans. Pattern Anal. Mach. Intell. 32(11), 2022–2038 (2010)

    Article  Google Scholar 

  70. Shih, F.Y., Chuang, C.F.: Automatic extraction of head and face boundaries and facial features. Inf. Sci. 158, 117–130 (2004)

    Article  Google Scholar 

  71. Wong, K.W., Lam, K.M., Siu, W.C.: An efficient algorithm for human face detection and facial feature extraction under different conditions. Pattern Recognit. 34(10), 1993–2004 (2001)

    Article  MATH  Google Scholar 

  72. Gorodnichy, D., Roth, G.: Nouse ‘use your nose as a mouse‘ perceptual vision technology for hands-free games and interfaces. Image Vis. Comput. 22(12), 931–942 (2004)

    Article  Google Scholar 

  73. Chang, K.I., Bowyer, K.W., Flynn, P.J.: Multiple nose region matching for 3D face recognition under varying facial expression. IEEE Trans. Pattern Anal. Mach. Intell. 28(10), 1695–1700 (2006)

    Article  Google Scholar 

  74. Song, J., Jia, L., Wang, W., Ying, H.: Robust nose tip localization based on two-stage subclass discriminant analysis. Neurocomputing 137, 173–179 (2014)

    Article  Google Scholar 

  75. Bevilacqua, V., Ciccimarra, A., Leone, I., Mastronardi, G.: Automatic facial feature points detection. Lecture Notes in Artificial Intelligence (LNAI), vol. 5227, pp. 1142–1149 (2008)

    Google Scholar 

  76. Hassaballah, M., Murakami, K., Ido, S.: Eye and nose fields detection from gray scale facial images. In: MVA, pp. 406–409 (2011)

    Google Scholar 

  77. Gizatdinova, Y., Surakka, V.: Feature-based detection of facial landmarks from neutral and expressive facial images. IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 135–139 (2006)

    Article  Google Scholar 

  78. Hassaballah, M., Kanazawa, T., Ido, S., Ido, S.: A robust method for nose detection under various conditions. In: International Conference on Computer Vision and Graphics, pp. 392–400. Springer (2010)

    Google Scholar 

  79. Xu, C., Wang, Y., Tan, T., Quan, L.: Robust nose detection in 3D facial data using local characteristics. In: International Conference on Image Processing (ICIP’04), pp. 1995–1998, Singapore, 24–27 Oct 2004

    Google Scholar 

  80. Chew, W.J., Seng, K.P., Ang, L.M.: Nose tip detection on a three-dimensional face range image invariant to head pose. In: Proceedings of the International MultiConference of Engineers and Computer Scientists (IMECS’09), pp. 858–862, Hong Kong, China, 18–20 Mar 2009

    Google Scholar 

  81. Xu, C., Tan, T., Wang, Y., Quan, L.: Combining local features for robust nose location in 3D facial data. Pattern Recognit. Lett. 27(13), 1487–1494 (2006)

    Article  Google Scholar 

  82. Zheng, Z., Yang, J., Yang, L.: A robust method for eye features extraction on color image. Pattern Recognit. Lett. 26(14), 2252–2261 (2005)

    Article  Google Scholar 

  83. Mayer, C., Wimmer, M., Radig, B.: Adjusted pixel features for robust facial component classification. Image Vis. Comput. 28(5), 762–771 (2010)

    Article  Google Scholar 

  84. Xiong, X., De la Torre, F.: Supervised descent method and its applications to face alignment. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 532–539 (2013)

    Google Scholar 

  85. Benitez-Quiroz, C.F., Rivera, S., Gotardo, P.F., Martinez, A.M.: Salient and non-salient fiducial detection using a probabilistic graphical model. Pattern Recognit. 47(1), 208–215 (2014)

    Article  Google Scholar 

  86. Wang, N., Gao, X., Tao, D., Yang, H., Li, X.: Facial feature point detection: a comprehensive survey. Neurocomputing 275, 50–65 (2018)

    Article  Google Scholar 

  87. Belhumeur, P.N., Jacobs, D.W., Kriegman, D.J., Kumar, N.: Localizing parts of faces using a consensus of exemplars. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2930–2940 (2013)

    Article  Google Scholar 

  88. Rivera, S., Martinez, A.M.: Precise fiducial detection. Encyclopedia of Biometrics, pp. 1268–1271 (2015)

    Chapter  Google Scholar 

  89. Chow, G., Li, X.: Toward a system for automatic facial feature detection. Pattern Recognit. 26(12), 1739–1755 (1993)

    Article  Google Scholar 

  90. Kawaguchi, T., Rizon, M., Hidaka, D.: Detection of eyes from human faces by hough transform and separability filter. Electron. Commun. Jpn. Part 2 88(5), 2190–2200 (2005)

    Google Scholar 

  91. Feng, G.C., Yuen, P.C.: Variance projection function and its application to eye detection for human face recognition. Pattern Recognit. Lett. 19(9), 899–906 (1998)

    Article  Google Scholar 

  92. Zhou, Z.H., Geng, X.: Projection functions for eye detection. Pattern Recognit. 37(5), 1049–1056 (2004)

    Article  MATH  Google Scholar 

  93. Asteriadis, S., Nikolaidis, N., Pitas, I.: Facial feature detection using distance vector fields. Pattern Recognit. 42(7), 1388–1398 (2009)

    Article  MATH  Google Scholar 

  94. Wan, K.W., Lam, K.M., Ng, K.C.: An accurate active shape model for facial feature extraction. Pattern Recognit. Lett. 26(15), 2409–2423 (2005)

    Article  Google Scholar 

  95. Cristinacce, D., Cootes, T.: Automatic feature localisation with constrained local models. Pattern Recognit. 41(10), 3054–3067 (2008)

    Article  MATH  Google Scholar 

  96. Yang, H., Patras, I.: Privileged information-based conditional regression forest for facial feature detection. In: 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1–6. IEEE (2013)

    Google Scholar 

  97. Yang, H., Patras, I.: Sieving regression forest votes for facial feature detection in the wild. In: IEEE International Conference on Computer Vision (ICCV), pp. 1936–1943. IEEE (2013)

    Google Scholar 

  98. Lindner, C., Bromiley, P.A., Ionita, M.C., Cootes, T.F.: Robust and accurate shape model matching using random forest regression-voting. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1862–1874 (2015)

    Article  Google Scholar 

  99. Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3476–3483. IEEE (2013)

    Google Scholar 

  100. Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., Lew, M.S.: Deep learning for visual understanding: a review. Neurocomputing 187, 27–48 (2016)

    Article  Google Scholar 

  101. Zhang, Z., Luo, P., Loy, C.C., Tang, X.: Learning deep representation for face alignment with auxiliary attributes. IEEE Trans. Pattern Anal. Mach. Intell. 38(5), 918–930 (2016)

    Article  Google Scholar 

  102. Zhou, E., Fan, H., Cao, Z., Jiang, Y., Yin, Q.: Extensive facial landmark localization with coarse-to-fine convolutional network cascade. In: IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 386–391. IEEE (2013)

    Google Scholar 

  103. Lai, H., Xiao, S., Pan, Y., Cui, Z., Feng, J., Xu, C., Yin, J., Yan, S.: Deep recurrent regression for facial landmark detection. IEEE Trans. Circ. Syst. Video Technol. (2018)

    Google Scholar 

  104. 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 

  105. Wu, Y., Ji, Q.: Discriminative deep face shape model for facial point detection. Int. J. Comput. Vis. 113(1), 37–53 (2015)

    Article  MathSciNet  Google Scholar 

  106. He, Z., Zhang, J., Kan, M., Shan, S., Chen, X.: Robust FEC-CNN: a high accuracy facial landmark detection system. In: Proceedings of the International Conference on Computer Vision & Pattern Recognition (CVPRW), Faces-in-the-wild Workshop/Challenge, vol. 3, p. 6 (2017)

    Google Scholar 

  107. Chen, X., Zhou, E., Liu, J., Mo, Y.: Delving deep into coarse-to-fine framework for facial landmark localization. In: Proceedings of the International Conference on Computer Vision & Pattern Recognition (CVPRW), Faces-in-the-wild Workshop/Challenge (2017)

    Google Scholar 

  108. Yang, J., Liu, Q., Zhang, K.: Stacked hourglass network for robust facial landmark localisation. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2025–2033. IEEE (2017)

    Google Scholar 

  109. Fan, X., Liu, R., Luo, Z., Li, Y., Feng, Y.: Explicit shape regression with characteristic number for facial landmark localization. IEEE Trans. Multimed. 20(3), 567–579 (2018)

    Article  Google Scholar 

  110. Zeng, J., Liu, S., Li, X., Mahdi, D.A., Wu, F., Wang, G.: Deep context-sensitive facial landmark detection with tree-structured modeling. IEEE Trans. Image Process. 27(5), 2096–2107 (2018)

    Article  MathSciNet  Google Scholar 

  111. Deng, W., Fang, Y., Xu, Z., Hu, J.: Facial landmark localization by enhanced convolutional neural network. Neurocomputing 273, 222–229 (2018)

    Article  Google Scholar 

  112. Messer, K., Matas, J., Kittler, J., Luettin, J., Maitre, G.: XM2VTSDB, the extended M2VTS database. In: 2nd International Conference on Audio and Video-based Biometric Person Authentication Recognition (AVBPA’99), pp. 72–77, Washington DC., USA, 22–24 Mar 1999

    Google Scholar 

  113. Phillips, P.J., Moon, H., Rizvi, S., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090–1104 (2000)

    Article  Google Scholar 

  114. Lyons, M.J., Budynek, J., Akamatsu, S.: Automatic classification of single facial images. IEEE Trans. Pattern Anal. Mach. Intell. 21(12), 1357–1362 (1999)

    Article  Google Scholar 

  115. Kasinski, A., Florek, A., Schmidt, A.: The PUT face database. Image Process. Commun. 13(3–4), 59–64 (2008)

    Google Scholar 

  116. Samaria, F.S., Harter, A.C.: Parameterisation of a stochastic model for human face identification. In: IEEE Workshop on Applications of Computer Vision, pp. 138–142. IEEE (1994)

    Google Scholar 

  117. Nordstrøm, M.M., Larsen, M., Sierakowski, J., Stegmann, M.B.: The IMM face database-an annotated dataset of 240 face images (2004)

    Google Scholar 

  118. Milborrow, S., Morkel, J., Nicolls, F.: The MUCT Landmarked Face Database. Pattern Recognit. Assoc. S. Afr. (2010). http://www.milbo.org/muct

  119. Aifanti, N., Papachristou, C., Delopoulos, A.: The MUG facial expression database. In: 11th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), pp. 1–4. IEEE (2010)

    Google Scholar 

  120. Gao, W., Cao, B., Shan, S., Chen, X., Zhou, D., Zhang, X., Zhao, D.: The CAS-PEAL large-scale chinese face database and baseline evaluations. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 38(1), 149–161 (2008)

    Google Scholar 

  121. 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 (ICCV Workshops), pp. 2144–2151. IEEE (2011)

    Google Scholar 

  122. Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 643–660 (2001)

    Article  Google Scholar 

  123. 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 

  124. Frischholz, R.W., Dieckmann, U.: BioID: a multimodal biometric identification system. IEEE Comput. 33(2), 64–68 (2000)

    Article  Google Scholar 

  125. Zhu, M., Martinez, A.: Subclass discriminant analysis. IEEE Trans. Pattern Anal. Mach. Intell. 28(8), 1274–1286 (2006)

    Article  Google Scholar 

  126. Hassaballah, M., Awad, A.I.: Detection and description of image features: an introduction. Image Feature Detectors and Descriptors, pp. 1–8. Springer (2016)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Computers and Information, Computer Science Department, South Valley University, Luxor, Egypt

    M. Hassaballah

  2. Faculty of Commerce, South Valley University, Qena, Egypt

    Saddam Bekhet

  3. Faculty of Science, Mathematics Department, South Valley University, Qena, 83523, Egypt

    Amal A. M. Rashed

  4. School of Information Science and Engineering, Shenyang University of Technology, Shenyang, 110023, China

    Gang Zhang

Authors
  1. M. Hassaballah
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  2. Saddam Bekhet
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  3. Amal A. M. Rashed
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  4. Gang Zhang
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Corresponding author

Correspondence to M. Hassaballah .

Editor information

Editors and Affiliations

  1. Department of Computer Science, South Valley University, Luxor, Egypt

    Mahmoud Hassaballah

  2. Department of Information Technology, Zagazig University, Zagazig, Egypt

    Khalid M. Hosny

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Hassaballah, M., Bekhet, S., Rashed, A.A.M., Zhang, G. (2019). Facial Features Detection and Localization. In: Hassaballah, M., Hosny, K. (eds) Recent Advances in Computer Vision. Studies in Computational Intelligence, vol 804. Springer, Cham. https://doi.org/10.1007/978-3-030-03000-1_2

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