{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T00:19:47Z","timestamp":1775261987252,"version":"3.50.1"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030033378","type":"print"},{"value":"9783030033385","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-030-03338-5_17","type":"book-chapter","created":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T23:57:42Z","timestamp":1541116662000},"page":"195-206","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Score-Guided Face Alignment Network Under Occlusions"],"prefix":"10.1007","author":[{"given":"Xiang","family":"Yan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huabin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinjie","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Tao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,11,3]]},"reference":[{"issue":"11","key":"17_CR1","doi-asserted-by":"publisher","first-page":"4311","DOI":"10.1109\/TSP.2006.881199","volume":"54","author":"M Aharon","year":"2006","unstructured":"Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Sig. Process. 54(11), 4311\u20134322 (2006)","journal-title":"IEEE Trans. Sig. Process."},{"key":"17_CR2","doi-asserted-by":"crossref","unstructured":"Belhumeur, P.N., Jacobs, D.W., Kriegman, D.J., Kumar, N.: Localizing parts of faces using a consensus of exemplars. In: Computer Vision and Pattern Recognition (CVPR), pp. 545\u2013552 (2011)","DOI":"10.1109\/CVPR.2011.5995602"},{"key":"17_CR3","unstructured":"Bettadapura, V.: Face expression recognition and analysis: the state of the art (2012). arXiv preprint arXiv:1203.6722"},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Bulat, A., Tzimiropoulos, G.: Convolutional aggregation of local evidence for large pose face alignment (2016)","DOI":"10.5244\/C.30.86"},{"key":"17_CR5","doi-asserted-by":"crossref","unstructured":"Bulat, A., Tzimiropoulos, G.: Binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources. In: The IEEE International Conference on Computer Vision (ICCV), vol. 1, p. 4 (2017)","DOI":"10.1109\/ICCV.2017.400"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Bulat, A., Tzimiropoulos, G.: How far are we from solving the 2D and 3D face alignment problem? (and a dataset of 230,000 3D facial landmarks). In: International Conference on Computer Vision (ICCV), vol. 1, p. 4 (2017)","DOI":"10.1109\/ICCV.2017.116"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Burgos-Artizzu, X.P., Perona, P.: Robust face landmark estimation under occlusion. In: International Conference on Computer Vision (ICCV), pp. 1513\u20131520 (2013)","DOI":"10.1109\/ICCV.2013.191"},{"issue":"2","key":"17_CR8","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/s11263-013-0667-3","volume":"107","author":"X Cao","year":"2014","unstructured":"Cao, X., Wei, Y., Wen, F., Sun, J.: Face alignment by explicit shape regression. Int. J. Comput. Vis. 107(2), 177\u2013190 (2014)","journal-title":"Int. J. Comput. Vis."},{"key":"17_CR9","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.imavis.2015.11.005","volume":"47","author":"J Deng","year":"2016","unstructured":"Deng, J., Liu, Q., Yang, J., Tao, D.: M3 CSR: Multi-view, multi-scale and multi-component cascade shape regression. Image Vis. Comput. 47, 19\u201326 (2016)","journal-title":"Image Vis. Comput."},{"key":"17_CR10","unstructured":"Deng, J., Trigeorgis, G., Zhou, Y., Zafeiriou, S.: Joint multi-view face alignment in the wild (2017). arXiv preprint arXiv:1708.06023"},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Feng, Z.H., Kittler, J., Christmas, W., Huber, P., Wu, X.J.: Dynamic attention-controlled cascaded shape regression exploiting training data augmentation and fuzzy-set sample weighting. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3681\u20133690. IEEE (2017)","DOI":"10.1109\/CVPR.2017.392"},{"key":"17_CR12","unstructured":"Ghiasi, G., Fowlkes, C.C.: Occlusion coherence: detecting and localizing occluded faces (2015). arXiv preprint arXiv:1506.08347"},{"issue":"5","key":"17_CR13","doi-asserted-by":"publisher","first-page":"1977","DOI":"10.1109\/TIP.2016.2537215","volume":"25","author":"Y Guo","year":"2016","unstructured":"Guo, Y., Zhao, G., Pietik\u00e4inen, M.: Dynamic facial expression recognition with atlas construction and sparse representation. IEEE Trans. Image Process. 25(5), 1977\u20131992 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"17_CR15","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1007\/978-3-642-33712-3_49","volume-title":"Computer Vision \u2013 ECCV 2012","author":"Vuong Le","year":"2012","unstructured":"Le, V., Brandt, J., Bourdev, L., Bourdev, L., Huang, T.S.: Interactive facial feature localization. In: European Conference on Computer Vision (ECCV), pp. 679\u2013692 (2012)"},{"issue":"8","key":"17_CR16","doi-asserted-by":"publisher","first-page":"2317","DOI":"10.1109\/TIP.2015.2412374","volume":"24","author":"D Li","year":"2015","unstructured":"Li, D., Zhou, H., Lam, K.M.: High-resolution face verification using pore-scale facial features. IEEE Trans. Image Process. 24(8), 2317\u20132327 (2015)","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"17_CR17","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1109\/TIP.2015.2502485","volume":"25","author":"Q Liu","year":"2016","unstructured":"Liu, Q., Deng, J., Tao, D.: Dual sparse constrained cascade regression for robust face alignment. IEEE Trans. Image Process. 25(2), 700\u2013712 (2016)","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"17_CR18","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1109\/TIP.2016.2633939","volume":"26","author":"Q Liu","year":"2017","unstructured":"Liu, Q., Deng, J., Yang, J., Liu, G., Tao, D.: Adaptive cascade regression model for robust face alignment. IEEE Trans. Image Process.(TIP) 26(2), 797\u2013807 (2017)","journal-title":"IEEE Trans. Image Process.(TIP)"},{"key":"17_CR19","doi-asserted-by":"crossref","unstructured":"Lv, J.J., Shao, X., Xing, J., Cheng, C., Zhou, X., et al.: A deep regression architecture with two-stage re-initialization for high performance facial landmark detection. In: Computer Vision and Pattern Recognition (CVPR), vol. 1, p. 4 (2017)","DOI":"10.1109\/CVPR.2017.393"},{"key":"17_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1007\/978-3-319-46484-8_29","volume-title":"Computer Vision \u2013 ECCV 2016","author":"A Newell","year":"2016","unstructured":"Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483\u2013499. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_29"},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Ranjan, R., Sankaranarayanan, S., Castillo, C.D., Chellappa, R.: An all-in-one convolutional neural network for face analysis. In: 2017 12th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2017), pp. 17\u201324. IEEE (2017)","DOI":"10.1109\/FG.2017.137"},{"issue":"3","key":"17_CR22","doi-asserted-by":"publisher","first-page":"1233","DOI":"10.1109\/TIP.2016.2518867","volume":"25","author":"S Ren","year":"2016","unstructured":"Ren, S., Cao, X., Wei, Y., Sun, J.: Face alignment via regressing local binary features. IEEE Trans. Image Process.(TIP) 25(3), 1233 (2016)","journal-title":"IEEE Trans. Image Process.(TIP)"},{"key":"17_CR23","doi-asserted-by":"crossref","unstructured":"Ren, S., Cao, X., Wei, Y., Sun, J.: Face alignment at 3000 fps via regressing local binary features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1685\u20131692 (2014)","DOI":"10.1109\/CVPR.2014.218"},{"key":"17_CR24","doi-asserted-by":"crossref","unstructured":"Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: 300 faces in-the-wild challenge: the first facial landmark localization challenge. In: Conference on Computer Vision Workshops (CVPRW), pp. 397\u2013403 (2014)","DOI":"10.1109\/ICCVW.2013.59"},{"key":"17_CR25","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: Computer Vision and Pattern Recognition (CVPR), pp. 3476\u20133483 (2013)","DOI":"10.1109\/CVPR.2013.446"},{"issue":"6","key":"17_CR26","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/TIP.2016.2551362","volume":"25","author":"Y Tai","year":"2016","unstructured":"Tai, Y., Yang, J., Zhang, Y., Luo, L., Qian, J., Chen, Y.: Face recognition with pose variations and misalignment via orthogonal procrustes regression. IEEE Trans. Image Process. 25(6), 2673\u20132683 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"17_CR27","doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701\u20131708 (2014)","DOI":"10.1109\/CVPR.2014.220"},{"key":"17_CR28","doi-asserted-by":"crossref","unstructured":"Trigeorgis, G., Snape, P., Nicolaou, M.A., Antonakos, E., Zafeiriou, S.: Mnemonic descent method: a recurrent process applied for end-to-end face alignment. In: Computer Vision and Pattern Recognition (CVPR), pp. 4177\u20134187 (2016)","DOI":"10.1109\/CVPR.2016.453"},{"key":"17_CR29","doi-asserted-by":"crossref","unstructured":"Wu, Y., Ji, Q.: Robust facial landmark detection under significant head poses and occlusion. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3658\u20133666 (2015)","DOI":"10.1109\/ICCV.2015.417"},{"key":"17_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/978-3-319-46448-0_4","volume-title":"Computer Vision \u2013 ECCV 2016","author":"S Xiao","year":"2016","unstructured":"Xiao, S., Feng, J., Xing, J., Lai, H., Yan, S., Kassim, A.: Robust facial landmark detection via recurrent attentive-refinement networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 57\u201372. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_4"},{"key":"17_CR31","doi-asserted-by":"crossref","unstructured":"Xiong, X., Torre, F.D.L.: Supervised descent method and its applications to face alignment. In: Computer Vision and Pattern Recognition (CVPR), pp. 532\u2013539 (2013)","DOI":"10.1109\/CVPR.2013.75"},{"key":"17_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/978-3-319-46454-1_4","volume-title":"Computer Vision \u2013 ECCV 2016","author":"X Yu","year":"2016","unstructured":"Yu, X., Zhou, F., Chandraker, M.: Deep deformation network for object landmark localization. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 52\u201370. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46454-1_4"},{"key":"17_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, J., Kan, M., Shan, S., Chen, X.: Occlusion-free face alignment: deep regression networks coupled with de-corrupt autoencoders. In: Computer Vision and Pattern Recognition (CVPR), pp. 3428\u20133437 (2016)","DOI":"10.1109\/CVPR.2016.373"},{"key":"17_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-319-10605-2_1","volume-title":"Computer Vision \u2013 ECCV 2014","author":"J Zhang","year":"2014","unstructured":"Zhang, J., Shan, S., Kan, M., Chen, X.: Coarse-to-fine auto-encoder networks (CFAN) for real-time face alignment. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 1\u201316. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10605-2_1"},{"key":"17_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1007\/978-3-319-10599-4_7","volume-title":"Computer Vision \u2013 ECCV 2014","author":"Z Zhang","year":"2014","unstructured":"Zhang, Z., Luo, P., Loy, C.C., Tang, X.: Facial landmark detection by deep multi-task learning. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 94\u2013108. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10599-4_7"},{"key":"17_CR36","doi-asserted-by":"crossref","unstructured":"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\u2013391 (2013)","DOI":"10.1109\/ICCVW.2013.58"},{"key":"17_CR37","doi-asserted-by":"crossref","unstructured":"Zhu, S., Li, C., Chen, C.L., Tang, X.: Face alignment by coarse-to-fine shape searching. In: Computer Vision and Pattern Recognition (CVPR), pp. 4998\u20135006 (2015)","DOI":"10.1109\/CVPR.2015.7299134"},{"key":"17_CR38","doi-asserted-by":"crossref","unstructured":"Zhu, S., Li, C., Loy, C.C., Tang, X.: Unconstrained face alignment via cascaded compositional learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3409\u20133417 (2016)","DOI":"10.1109\/CVPR.2016.371"},{"key":"17_CR39","doi-asserted-by":"crossref","unstructured":"Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: Computer Vision and Pattern Recognition (CVPR), pp. 2879\u20132886. IEEE (2012)","DOI":"10.1109\/CVPR.2012.6248014"},{"key":"17_CR40","doi-asserted-by":"crossref","unstructured":"Zhu, X., Lei, Z., Liu, X., Shi, H., Li, S.Z.: Face alignment across large poses: a 3D solution. In: Proceedings of the IEEE Conference on Computer Vision And Pattern Recognition, pp. 146\u2013155 (2016)","DOI":"10.1109\/CVPR.2016.23"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-03338-5_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T23:05:37Z","timestamp":1775257537000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-03338-5_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030033378","9783030033385"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-03338-5_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"3 November 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/prcv.qyhw.net.cn\/?lang=en&meeting_id=255","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}