Pattern Analysis and Applications

, Volume 20, Issue 4, pp 1169–1178 | Cite as

A clustered locally linear approach on face manifolds for pose estimation

Theoretical Advances
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Abstract

Data points with small variations between them are assumed to lie close to each other on a smooth varying manifold in the feature space. Such data are hard to classify into separate classes . A sequence of face pose images with closely varying pose angles can be considered as such data. The pose angles when large enough create images that are largely differing from each other, and thus, the sequence of face images can be assumed to be on or near a nonlinear manifold. In this paper, we propose an unsupervised pose estimation method for face images based on clustered locally linear manifolds using discriminant analysis. We divide the data into multiple disjointed, locally linear and separable clusters. The problem of identifying which cluster to use is solved by dividing the entire process into two steps. The first step or projection using the entire smooth manifold identifies a rough region of interest. We use clustering techniques on entire data to form the pose-dependent classes which are then used to find the first set of discriminant functions. The second step or second projection uses trained cluster(s) from this neighbourhood to obtain a second set of discriminant functions. The idea behind such an approach is that the local neighbourhood would be linear and provide better between-class separation, and hence, the classification problem would now be simpler.

Keywords

Pose estimation Clustering Smooth manifolds Discriminant analysis Multiple subspaces 

References

  1. 1.
    Murphy-Chutorian E, Trivedi MM (2009) Head pose estimation in computer vision: a survey. IEEE Trans Pattern Anal Mach Intell 31(4):607–626CrossRefGoogle Scholar
  2. 2.
    Czuprynski B, Strupczewski A (2014) High accuracy head pose tracking survey. In: Active media technology: 10th international conference, AMT 2014, Warsaw, Poland, August 11–14, 2014, Proceedings, vol 8610, Springer, Berlin, p 407Google Scholar
  3. 3.
    Niyogi S, Freeman WT (1996) Example-based head tracking. In: Proceedings of the second international conference on automatic face and gesture recognition, pp 374–378. IEEEGoogle Scholar
  4. 4.
    Beymer DJ (1994) Face recognition under varying pose. In: Proceedings CVPR’94., 1994 IEEE computer society conference on computer vision and pattern recognition, pp 756–761. IEEEGoogle Scholar
  5. 5.
    Demirkus M, Precup D, Clark JJ, Arbel T (2014) Probabilistic temporal head pose estimation using a hierarchical graphical model. In: Computer Vision–ECCV 2014, Springer, Berlin, pp 328–344Google Scholar
  6. 6.
    Cayton L (2005) Algorithms for manifold learning. University of California at San Diego Technical Report, pp 1–17Google Scholar
  7. 7.
    Sherrah J, Gong S, Ong E-J (2001) Face distributions in similarity space under varying head pose. Image Vis Comput 19(12):807–819CrossRefGoogle Scholar
  8. 8.
    Junwen W, Trivedi MM (2008) A two-stage head pose estimation framework and evaluation. Pattern Recogn 41(3):1138–1158CrossRefMATHGoogle Scholar
  9. 9.
    McKenna SJ, Gong S (1998) Real-time face pose estimation. Real-Time Imaging 4(5):333–347CrossRefGoogle Scholar
  10. 10.
    Duda RO, Hart PE, Stork DG (2012) Pattern classification. Wiley, New YorkMATHGoogle Scholar
  11. 11.
    Kwak N, Choi S-I, Choi C-H (2008) Feature extraction for regression problems and an example application for pose estimation of a face. In: Campilho A, Kamel M (eds) Image analysis and recognition, Springer, Berlin, pp 435–444CrossRefGoogle Scholar
  12. 12.
    Martínez AM, Kak AC (2001) Pca versus lda. IEEE Trans Pattern Anal Mach Intell 23(2):228–233CrossRefGoogle Scholar
  13. 13.
    Chen L, Zhang L, Hu Y, Li M, Zhang H (2003) Head pose estimation using fisher manifold learning. In: AMFG, pp 203–207Google Scholar
  14. 14.
    Martina U, Roth PM, Horst B (2009) Efficient classification for large-scale problems by multiple lda subspaces. In: VISAPP (1), pp 299–306Google Scholar
  15. 15.
    Sankaran P, Hari CV (2014) Multi subspace analysis with supervised separable clusters for classification of smooth nonlinear manifolds. In: Eighth international conference on image and signal processing (ICISP 2014)Google Scholar
  16. 16.
    Tenenbaum JB, De Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323CrossRefGoogle Scholar
  17. 17.
    Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326CrossRefGoogle Scholar
  18. 18.
    Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396CrossRefMATHGoogle Scholar
  19. 19.
    Raytchev B, Yoda I, Sakaue K (2004) Head pose estimation by nonlinear manifold learning. In: Proceedings of the 17th international conference on pattern recognition, ICPR 2004, vol 4, pp 462–466. IEEEGoogle Scholar
  20. 20.
    Hu N, Huang W, Ranganath S (2005) Head pose estimation by non-linear embedding and mapping. In: IEEE international conference on image processing, ICIP 2005, vol 2, pp II–342. IEEEGoogle Scholar
  21. 21.
    Balasubramanian VN, Krishna S, Panchanathan S (2008) Person-independent head pose estimation using biased manifold embedding. EURASIP J Adv Signal Process 2008:63CrossRefMATHGoogle Scholar
  22. 22.
    Lewandowski M, Makris D, Velastin SA, Nebel J-C (2014) Structural laplacian eigenmaps for modeling sets of multivariate sequences. IEEE Trans Cybern 44(6):936–949CrossRefGoogle Scholar
  23. 23.
    Balasubramanian VN, Ye J, Panchanathan S (2007) Biased manifold embedding: a framework for person-independent head pose estimation. In: IEEE conference on computer vision and pattern recognition, CVPR’07, pp 1–7. IEEEGoogle Scholar
  24. 24.
    Yan S , Zhang Z, Fu Y, Hu Y, Tu J, Huang TS (2007) Synchronized submanifold embedding for person-independent precise 3D pose estimation. Paper presented at the 2007 Global Infotech Conference, Urbana-Champaign, Illinois, 6 Sept 2007Google Scholar
  25. 25.
    Yan S, Wang H, Yun F, Yan J, Tang X, Huang TS (2009) Synchronized submanifold embedding for person-independent pose estimation and beyond. IEEE Trans Image Process 18(1):202–210CrossRefMATHGoogle Scholar
  26. 26.
    Zhu Y, Xue Z, Li C (2014) Automatic head pose estimation with synchronized sub manifold embedding and random regression forests. Int J Signal Process Image Process Pattern Recogn 7(3):123–134Google Scholar
  27. 27.
    Lawrence ND (2004) Gaussian process latent variable models for visualisation of high dimensional data. Adv Neural Inf process Syst 16(3):329–336Google Scholar
  28. 28.
    Foytik J, Asari VK (2013) A two-layer framework for piecewise linear manifold-based head pose estimation. Int J Comput Vis 101(2):270–287CrossRefGoogle Scholar
  29. 29.
    Rui X, Wunsch D et al (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16(3):645–678CrossRefGoogle Scholar
  30. 30.
    Berkhin P (2006) A survey of clustering data mining techniques. In: Grouping multidimensional data, Springer, Berlin, pp 25–71Google Scholar
  31. 31.
    Gose E, Johnsonbaugh R, Jost S (1996) Pattern recognition and image analysis. Prentice-Hall Inc, Upper Saddle RiverGoogle Scholar
  32. 32.
    Hari CV, Sankaran P (2014) Face pose estimation for driver distraction monitoring by automatic clustered linear discriminant analysis. In: IEEE international conference on vehicular electronics and safety (ICVES), pp 100–105. IEEEGoogle Scholar
  33. 33.
    Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720CrossRefGoogle Scholar
  34. 34.
    Black JA Jr, Gargesha M, Kahol K, Kuchi P, Panchanathan S (2002) A framework for performance evaluation of face recognition algorithms. In: Proceedings of SPIE, vol 4862, p 164Google Scholar
  35. 35.
    Little D, Sreekar K, John B, Sethuraman P (2005) A methodology for evaluating robustness of face recognition algorithms with respect to variations in pose angle and illumination angle. In: Proceedings of the IEEE international conference on acoustics, speech and signal processingGoogle Scholar
  36. 36.
    Nicolas G, Daniela H, Crowley JL (2004) Estimating face orientation from robust detection of salient facial features. In: ICPR International Workshop on Visual Observation of Deictic Gestures, CiteseerGoogle Scholar
  37. 37.
    BenAbdelkader C (2010) Robust head pose estimation using supervised manifold learning. In: Computer Vision–ECCV 2010, Springer, Berlin, pp 518–531Google Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringNational Institute of Technology CalicutKeralaIndia

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