Multimedia Tools and Applications

, Volume 75, Issue 19, pp 11801–11813 | Cite as

A fusion method for robust face tracking

  • Xiaodong JiangEmail author
  • Hui Yu
  • Yang Lu
  • Honghai Liu


Face tracking often encounters drifting problems, especially when a significant face appearance variation occurs. Many trackers suffer from the difficulty of facial feature extraction during a wide range of face turning, occlusion, and even invisibleness. In this paper, we propose a novel and efficient fusion strategy for robust face tracking. A Supervised Descent Method (SDM) and a Compressive Tracking method (CT) are employed at the same time. SDM is used to correct drifting errors of CT continuously during frontal face tracking. However, when the face orientation changes to the angle orthogonal to the view line, it results in tracking failure for the SDM method. CT is then adopted to keep the face region being tracked until SDM detects and tracks the face again. In the experiments, we test the proposed method for real-time tracking using several challenging sequences from recent literatures. The fusion strategy has achieved encouraging performance in terms of both efficiency and reliability.


Fusion algorithm Human face tracking Compressive tracking Supervised descend method 



This work is partially supported by the State Scholarship Fund of China. Y. Lu is supported by the Natural Science Foundation of Heilongjiang Province of China under Grant F201428 and the 12th Five-Year-Plan in Key Science and Technology Research of agricultural bureau in Heilongjiang province of China under Grant HNK125B-04-03.


  1. 1.
    Babenko B, Yang MH, Belongie S (2011) Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell 33(8):1619–1632CrossRefGoogle Scholar
  2. 2.
    Candes EJ, Tao T (2005) Decoding by linear programming. IEEE Trans Inf Theory 51(12):4203–4215MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Candes EJ, Tao T (2006) Near-optimal signal recovery from random projections: universal encoding strategies? IEEE Trans Inf Theory 52(12):5406–5425MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Demirkus M, Clark JJ, Arbel T (2014) Robust semi-automatic head pose labeling for real-world face video sequences. Multimed Tools Appl 70(1):495–523CrossRefGoogle Scholar
  5. 5.
    Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Grabner H, Leistner C, Bischof H (2008) Semi-supervised on-line boosting for robust tracking. Comput Vis Eccv 5302:234–247, Pt I, Proceedings Google Scholar
  7. 7.
  8. 8.
    Hu CL et al (2014) An effective head pose estimation approach using Lie Algebrized Gaussians based face representation. Multimed Tools Appl 73(3):1863–1884CrossRefGoogle Scholar
  9. 9.
    Huang C et al (2007) High-performance rotation invariant multiview face detection. IEEE Trans Pattern Anal Mach Intell 29(4):671–686CrossRefGoogle Scholar
  10. 10.
    Jones MVP (2003) Fast multi-view face detection. Technical Report TR2003-96, Mitsubishi Electric Research Laboratories, Jul. 2003Google Scholar
  11. 11.
    Kaneko T, Hori O (2002) Template update criterion for template matching of image sequences. 16th International Conference on Pattern Recognition, Vol Ii, Proceedings, 1–5Google Scholar
  12. 12.
    Li HX, Shen CH, Shi QF (2011) Real-time visual tracking using compressive sensing. 2011 I.E. Conf Comput Vis Pattern Recogn (Cvpr), 1305–1312Google Scholar
  13. 13.
    Matthews I, Ishikawa T, Baker S (2004) The template update problem. IEEE Trans Pattern Anal Mach Intell 26(6):810–815CrossRefGoogle Scholar
  14. 14.
    Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57(2):137–154CrossRefGoogle Scholar
  15. 15.
    Wright J et al (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227CrossRefGoogle Scholar
  16. 16.
    Wu Y, Lim J, Yang MH (2013) Online object tracking: a benchmark. 2013 I.E. Conf Comput Vis Pattern Recogn (Cvpr), 2411–2418Google Scholar
  17. 17.
    Xiong XH, De la Torre F (2013) Supervised descent method and its applications to face alignment. 2013 I.E. Conf Comput Vis Pattern Recogn (Cvpr), 532–539Google Scholar
  18. 18.
    Xu R, Gu XD, Wei BJ (2013) An improved real time compressive tracking. 2013 Second Iapr Asian Conference on Pattern Recognition (Acpr 2013), 2013:692–696Google Scholar
  19. 19.
    Yu H, Liu HH (2014) Regression-based facial expression optimization. IEEE Trans Human-Mach Syst 44(3):386–394CrossRefGoogle Scholar
  20. 20.
    Zeisl B, et al (2010) On-line semi-supervised multiple-instance boosting. 2010 I.E. Conf Comput Vis Pattern Recogn (Cvpr), 1879–1886Google Scholar
  21. 21.
    Zhang KH, Zhang L, Yang MH (2012) Real-time compressive tracking. Comput Vis Eccv 7574:864–877, Pt Iii Google Scholar
  22. 22.
    Zhang TZ et al (2012) Low-rank sparse learning for robust visual tracking. Comput Vis Eccv 7577:470–484, Pt Vi Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Changchun Institute of Optics, Fine Mechanics and PhysicsChinese Academy of SciencesChangchunChina
  2. 2.University of PortsmouthPortsmouthUK
  3. 3.Heilongjiang Bayi Agricultural UniversityHeilongjiangChina

Personalised recommendations