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A clustered locally linear approach on face manifolds for pose estimation

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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.

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References

  1. Murphy-Chutorian E, Trivedi MM (2009) Head pose estimation in computer vision: a survey. IEEE Trans Pattern Anal Mach Intell 31(4):607–626

    Article  Google Scholar 

  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 407

  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. IEEE

  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. IEEE

  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 328–344

  6. Cayton L (2005) Algorithms for manifold learning. University of California at San Diego Technical Report, pp 1–17

  7. Sherrah J, Gong S, Ong E-J (2001) Face distributions in similarity space under varying head pose. Image Vis Comput 19(12):807–819

    Article  Google Scholar 

  8. Junwen W, Trivedi MM (2008) A two-stage head pose estimation framework and evaluation. Pattern Recogn 41(3):1138–1158

    Article  MATH  Google Scholar 

  9. McKenna SJ, Gong S (1998) Real-time face pose estimation. Real-Time Imaging 4(5):333–347

    Article  Google Scholar 

  10. Duda RO, Hart PE, Stork DG (2012) Pattern classification. Wiley, New York

    MATH  Google Scholar 

  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 435–444

    Chapter  Google Scholar 

  12. Martínez AM, Kak AC (2001) Pca versus lda. IEEE Trans Pattern Anal Mach Intell 23(2):228–233

    Article  Google Scholar 

  13. Chen L, Zhang L, Hu Y, Li M, Zhang H (2003) Head pose estimation using fisher manifold learning. In: AMFG, pp 203–207

  14. Martina U, Roth PM, Horst B (2009) Efficient classification for large-scale problems by multiple lda subspaces. In: VISAPP (1), pp 299–306

  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)

  16. Tenenbaum JB, De Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323

    Article  Google Scholar 

  17. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326

    Article  Google Scholar 

  18. Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396

    Article  MATH  Google Scholar 

  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. IEEE

  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. IEEE

  21. Balasubramanian VN, Krishna S, Panchanathan S (2008) Person-independent head pose estimation using biased manifold embedding. EURASIP J Adv Signal Process 2008:63

    Article  MATH  Google Scholar 

  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):936–949

    Article  Google Scholar 

  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. IEEE

  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 2007

  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):202–210

    Article  MATH  MathSciNet  Google Scholar 

  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):123–134

    Google Scholar 

  27. Lawrence ND (2004) Gaussian process latent variable models for visualisation of high dimensional data. Adv Neural Inf process Syst 16(3):329–336

    Google Scholar 

  28. Foytik J, Asari VK (2013) A two-layer framework for piecewise linear manifold-based head pose estimation. Int J Comput Vis 101(2):270–287

    Article  MathSciNet  Google Scholar 

  29. Rui X, Wunsch D et al (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16(3):645–678

    Article  Google Scholar 

  30. Berkhin P (2006) A survey of clustering data mining techniques. In: Grouping multidimensional data, Springer, Berlin, pp 25–71

  31. Gose E, Johnsonbaugh R, Jost S (1996) Pattern recognition and image analysis. Prentice-Hall Inc, Upper Saddle River

    Google Scholar 

  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. IEEE

  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):711–720

    Article  Google Scholar 

  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 164

  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 processing

  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, Citeseer

  37. BenAbdelkader C (2010) Robust head pose estimation using supervised manifold learning. In: Computer Vision–ECCV 2010, Springer, Berlin, pp 518–531

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Calicut, Kerala, India

    C. V. Hari & Praveen Sankaran

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  1. C. V. Hari
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  2. Praveen Sankaran
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Correspondence to C. V. Hari.

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Hari, C.V., Sankaran, P. A clustered locally linear approach on face manifolds for pose estimation. Pattern Anal Applic 20, 1169–1178 (2017). https://doi.org/10.1007/s10044-016-0557-8

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