[1] WANG X, YANG M, SHEN L, et al. Robust local representation for face recognition with single sample per person[C]//Proceedings of the 20153rd Asian Conference on Pattern Recognition. Piscataway, NJ:IEEE, 2015:11-15. [2] 胡章芳,秦阳鸿.基于图割理论的尺度自适应人脸跟踪算法[J].计算机应用,2017,37(4):1189-1192.(HU Z F, QIN Y H. Scale-adaptive face tracking algorithm based on graph cuts theory[J]. Journal of Computer Applications, 2017, 37(4):1189-1192.) [3] 郝宁波,廖海滨.真实感3D人脸快速建模研究[J].计算机应用研究,2011,28(1):352-356.(HAO N B, LIAO H B. Rapid 3D modeling of personalized face[J]. Application Research of Computers, 2011, 28(1):352-356.) [4] COOTES T F, TAYLOR C J, COOPER D H, et al. Active shape models-their training and application[J]. Computer Vision and Image Understanding, 1995, 61(1):38-59. [5] COOTES T F, EDWARDS G J, TAYLOR C J. Active appearance models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(6):681-685. [6] BURGOSARTIZZU X P, PERONA P, DOLLAR P. Robust face landmark estimation under occlusion[C]//Proceedings of the 2013 IEEE International Conference on Computer Vision. Piscataway, NJ:IEEE, 2013, 1513-1520. [7] XIONG X, TORRE F D. Supervised descent method and its applications to face alignment[C]//Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2013:532-539. [8] CAO X, WEI Y, WEN F, et al. Face alignment by explicit shape regression[C]//Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2012:2578-2585. [9] YU X, HUANG J, ZHANG S, et al. Pose-free facial landmark fitting via optimized part mixture and cascaded deformable shape model[C]//Proceedings of the 2013 IEEE International Conference on Computer Vision. Piscataway, NJ:IEEE, 2013:1944-1951. [10] CRISTINACCE D, COOTES T. Automatic feature localizations with constrained local models[J]. Pattern Recognition, 2008, 41(10):3054-3067. [11] SARAGIH J M, LUCEY S, COHN J F. Face alignment through subspace constrained mean-shifts[C]//Proceedings of the 2009 IEEE International Conference on Computer Vision. Piscataway, NJ:IEEE, 2010:1034-1041. [12] SUN Y, WANG X, TANG X. Deep convolutional network cascade for facial point detection[C]//Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2013:3476-3483. [13] ALBARRAN I A C, MORALES-HEMANDEZ L A, JIMENEZ-ARTHUR M A, et al. Adaptive methodology to daytime and night environments for eye and mouth detection based on artificial vision[C]//Proceedings of the 2014 IEEE International Conference on Mechatronics. Piscataway, NJ:IEEE, 2014:9-13. [14] YAN J, LEI Z, YI D, et al. Learn to combine multiple hypotheses for accurate face alignment[C]//Proceedings of the 2013 IEEE International Conference on Computer Vision Workshops. Washington, DC:IEEE Computer Society, 2013:392-396. [15] TSOCHANTARIDIS I, HOFMANN T, JOACHIMS T, et al. Support vector machine learning for interdependent and structured output spaces[C]//Proceedings of the 21st International Conference on Machine Learning. New York:ACM, 2004:104. [16] HOFMANN T, JOACHIMS T, ALTUN Y, et al. Large margin methods for structured and interdependent output variables[J]. Journal of Machine Learning Research, 2005, 6(2):1453-1484. [17] JACOBS D W, KRIEGMAN D J, KUMAR N. Localizing parts of faces using a consensus of exemplars[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(12):545-552. [18] LIN Z, BOURDEV L, HUANG T S, et al. Interactive facial feature localization[C]//Proceedings of the 12th European Conference on Computer Vision. Berlin:Springer, 2012:679-692. [19] SAGONAS C, ZAFEIRIOU S, PANTIC M, et al. 300 faces in-the-wild challenge:the first facial landmark localization challenge[C]//Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Piscataway, NJ:IEEE, 2013:397-403. |