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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Park, C.W., Lee, T.: A robust facial feature detection on mobile robot platform. Mach. Vis. Appl. 21(6), 981–988 (2010)
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)
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)
Valenti, R., Sebe, N., Gevers, T.: What are you looking at? Int. J. Comput. Vis. 98(3), 324–334 (2012)
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)
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)
Campadelli, P., Lanzarotti, R.: Fiducial point localization in color images of face foregrounds. Image Vis. Comput. 22(11), 863–872 (2004)
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)
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)
Hassaballah, M., Aly, S.: Face recognition: challenges, achievements and future directions. IET Comput. Vis. 9(4), 614–626 (2015)
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)
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)
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)
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)
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)
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)
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)
Best-Rowden, L., Jain, A.K.: Longitudinal study of automatic face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(1), 148–162 (2018)
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)
Arca, S., Campadelli, P., Lanzarotti, R.: A face recognition system based on automatically determined facial fiducial points. Pattern Recognit. 39(3), 432–443 (2006)
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)
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)
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)
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)
Li, M., Cheung, Y.M.: Automatic lip localization under face illumination with shadow consideration. Signal Process. 89(12), 2425–2434 (2009)
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)
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
Lu, Y., Yan, J., Gu, K.: Review on automatic lip reading techniques. Int. J. Pattern Recognit. Artif. Intell. 1856007 (2017)
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)
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)
Tawari, A., Trivedi, M.M.: Face expression recognition by cross modal data association. IEEE Trans. Multimed. 15(7), 1543–1552 (2013)
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)
Shu, Z., Shechtman, E., Samaras, D., Hadap, S.: Eyeopener: editing eyes in the wild. ACM Trans. Graph. (TOG) 36(1), 1 (2017)
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
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)
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)
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)
Kawulok, M., Celebi, E., Smolka, B.: Advances in Face Detection and Facial Image Analysis. Springer (2016)
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)
Feng, G., Yuen, P.: Multi-cues eye detection on gray intensity image. Pattern Recognit. 34(5), 1033–1046 (2001)
Qiong, W., Yang, J.: Eye detection in facial images with unconstrained background. J. Pattern Recognit. Res. 1(1), 55–62 (2006)
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)
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
Qian, Z., Xu, D.: Automatic eye detection using intensity filtering and k-means clustering. Pattern Recognit. Lett. 31(12), 1633–1640 (2010)
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)
Yuille, A., Hallinan, P., Cohen, D.: Feature extraction from faces using deformable templates. Int. J. Comput. Vis. 8(2), 99–111 (1992)
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)
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)
Wang, J.W., Chen, W.Y.: Eye detection based on head contour geometry and wavelet subband projection. Opt. Eng. 45(5), 57001–57013 (2006)
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
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)
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)
Jian, M., Lam, K.M., Dong, J.: Facial-feature detection and localization based on a hierarchical scheme. Inf. Sci. 262, 1–14 (2014)
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)
Chen, S., Liu, C.: Eye detection using discriminatory haar features and a new efficient SVM. Image Vis. Comput. 33, 68–77 (2015)
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
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)
Wang, P., Ji, Q.: Multi-view face and eye detection using discriminant features. Comput. Vis. Image Underst. 105(2), 99–111 (2007)
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
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)
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)
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)
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)
Matthews, I., Cootes, T., Bangham, J.: Extraction of visual features for lip reading. IEEE Trans. Pattern Anal. Mach. Intell. 24(2), 198–213 (2002)
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
Nakata, Y., Ando, M.: Lipreading method using color extraction method and eigenspace technique. Syst. Comput. Jpn 35(3), 12–23 (2004)
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
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)
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)
Shih, F.Y., Chuang, C.F.: Automatic extraction of head and face boundaries and facial features. Inf. Sci. 158, 117–130 (2004)
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)
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)
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)
Song, J., Jia, L., Wang, W., Ying, H.: Robust nose tip localization based on two-stage subclass discriminant analysis. Neurocomputing 137, 173–179 (2014)
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)
Hassaballah, M., Murakami, K., Ido, S.: Eye and nose fields detection from gray scale facial images. In: MVA, pp. 406–409 (2011)
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)
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)
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
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
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)
Zheng, Z., Yang, J., Yang, L.: A robust method for eye features extraction on color image. Pattern Recognit. Lett. 26(14), 2252–2261 (2005)
Mayer, C., Wimmer, M., Radig, B.: Adjusted pixel features for robust facial component classification. Image Vis. Comput. 28(5), 762–771 (2010)
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)
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)
Wang, N., Gao, X., Tao, D., Yang, H., Li, X.: Facial feature point detection: a comprehensive survey. Neurocomputing 275, 50–65 (2018)
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)
Rivera, S., Martinez, A.M.: Precise fiducial detection. Encyclopedia of Biometrics, pp. 1268–1271 (2015)
Chow, G., Li, X.: Toward a system for automatic facial feature detection. Pattern Recognit. 26(12), 1739–1755 (1993)
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)
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)
Zhou, Z.H., Geng, X.: Projection functions for eye detection. Pattern Recognit. 37(5), 1049–1056 (2004)
Asteriadis, S., Nikolaidis, N., Pitas, I.: Facial feature detection using distance vector fields. Pattern Recognit. 42(7), 1388–1398 (2009)
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)
Cristinacce, D., Cootes, T.: Automatic feature localisation with constrained local models. Pattern Recognit. 41(10), 3054–3067 (2008)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Wu, Y., Ji, Q.: Discriminative deep face shape model for facial point detection. Int. J. Comput. Vis. 113(1), 37–53 (2015)
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)
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)
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)
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)
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)
Deng, W., Fang, Y., Xu, Z., Hu, J.: Facial landmark localization by enhanced convolutional neural network. Neurocomputing 273, 222–229 (2018)
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
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)
Lyons, M.J., Budynek, J., Akamatsu, S.: Automatic classification of single facial images. IEEE Trans. Pattern Anal. Mach. Intell. 21(12), 1357–1362 (1999)
Kasinski, A., Florek, A., Schmidt, A.: The PUT face database. Image Process. Commun. 13(3–4), 59–64 (2008)
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)
Nordstrøm, M.M., Larsen, M., Sierakowski, J., Stegmann, M.B.: The IMM face database-an annotated dataset of 240 face images (2004)
Milborrow, S., Morkel, J., Nicolls, F.: The MUCT Landmarked Face Database. Pattern Recognit. Assoc. S. Afr. (2010). http://www.milbo.org/muct
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)
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)
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)
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)
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)
Frischholz, R.W., Dieckmann, U.: BioID: a multimodal biometric identification system. IEEE Comput. 33(2), 64–68 (2000)
Zhu, M., Martinez, A.: Subclass discriminant analysis. IEEE Trans. Pattern Anal. Mach. Intell. 28(8), 1274–1286 (2006)
Hassaballah, M., Awad, A.I.: Detection and description of image features: an introduction. Image Feature Detectors and Descriptors, pp. 1–8. Springer (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-03000-1_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02999-9
Online ISBN: 978-3-030-03000-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)