International Journal of Computer Vision

, Volume 101, Issue 2, pp 270–287 | Cite as

A Two-Layer Framework for Piecewise Linear Manifold-Based Head Pose Estimation



Fine-grain head pose estimation from imagery is an essential operation for many human-centered systems, including pose independent face recognition and human-computer interaction (HCI) systems. It is only recently that estimation systems have evolved past coarse level classification of pose and concentrated on fine-grain estimation. In particular, the state of the art of such systems consists of nonlinear manifold embedding techniques that capture the intrinsic relationship of a pose varying face dataset. The success of these solutions can be attributed to the acknowledgment that image variation corresponding to pose change is nonlinear in nature. Yet, the algorithms are limited by the complexity of embedding functions that describe the relationship. We present a pose estimation framework that seeks to describe the global nonlinear relationship in terms of localized linear functions. A two layer system (coarse/fine) is formulated on the assumptions that coarse pose estimation can be performed adequately using supervised linear methods, and fine pose estimation can be achieved using linear regressive functions if the scope of the pose manifold is limited. A pose estimation system is implemented utilizing simple linear subspace methods and oriented Gabor and phase congruency features. The framework is tested using widely accepted pose-varying face databases (FacePix(30) and Pointing’04) and shown to perform fine head pose estimation with competitive accuracy when compared with state of the art nonlinear manifold methods.


Head pose estimation Piecewise linear manifold Coarse to fine Phase congruency Gabor filter 


  1. Balasubramanian, V. N., 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 (pp. 1–7). Google Scholar
  2. Belhumeur, J.H., & Kriegman, D. (1997). Eigenfaces vs. fisherfaces: recognition using class-specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19, 711–720. CrossRefGoogle Scholar
  3. BenAbdelkader, C. (2010). Robust head pose estimation using supervised manifold learning. In ECCV’10. Proceedings of the 11th European conference on computer vision: part VI (pp. 518–531). Berlin: Springer. Google Scholar
  4. Foytik, J., Asari, V., Tompkins, R., & Youssef, M. (2010). Phase space for face pose estimation. In Advances in visual computing (pp. 49–58). CrossRefGoogle Scholar
  5. Fu, Y., & Huang, T. S. (2006). Graph embedded analysis for head pose estimation. In IEEE international conference on automatic face and gesture recognition (pp. 3–8). Google Scholar
  6. Gourier, N., Hall, D., & Crowley, J. L. (2004). Estimating face orientation from robust detection of salient facial features. In Proceedings of Pointing 2004, ICPR, international workshop on visual observation of deictic gestures. Google Scholar
  7. Gourier, N., Maisonnasse, J., Hall, D., & Crowley, J. L. (2006). Head pose estimation on low resolution images. In CLEAR 2006, South. Google Scholar
  8. Haj, M. A., Gonzalez, J., & Davis, L. S. (2012). On partial least squares in head pose estimation: how to simultaneously deal with misalignment. In 25th IEEE computer vision and pattern recognition. Google Scholar
  9. Kovesi, P. (1999). Image features from phase congruency. In VIDERE journal of computer vision research (Vol. 1). Google Scholar
  10. Li, Z., Fu, Y., Yuan, J., Huang, T. S., & Wu, Y. (2007). Query driven localized linear discriminant models for head pose estimation. In IEEE international conference on multimedia and expo (pp. 1810–1813). Google Scholar
  11. Little, G., Krishna, S., Black, J., & Panchanathan, S. (2005). A methodology for evaluating robustness of face recognition algorithms with respect to changes in pose and illumination angle. In IEEE international conference on acoustics, speech, and signal processing (pp. 89–92). Google Scholar
  12. Ma, B., Zhang, W., Shan, S., Chen, X., & Gao, W. (2006). Robust head pose estimation using lgbp. In International conference on pattern recognition (pp. 512–515). Google Scholar
  13. Ma, B., Shan, S., Chen, X., & Gao, W. (2008). Head yaw estimation from asymmetry of facial appearance. IEEE Transactions on Systems, Man and Cybernetics, 38, 1501–1512. CrossRefGoogle Scholar
  14. Melzer, T., Reiter, M., & Bischof, H. (2003). Appearance models based on kernel canonical correlation analysis. In Pattern recognition (pp. 1961–1971). Google Scholar
  15. Murphy-Chutorian, E., & Trivedi, M. M. (2008). Head pose estimation in computer vision: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31, 607–626. CrossRefGoogle Scholar
  16. Murphy-Chutorian, E., Doshi, A., & Trivedi, M. (2007). Head pose estimation for driver assistance systems: a robust algorithm and experimental evaluation. In Intelligent transportation systems conference (pp. 709–714). Google Scholar
  17. Oppenheim, A., & Lim, J. (1981). The importance of phase in signals. Proceedings of the IEEE, 69, 529–541. CrossRefGoogle Scholar
  18. Sherrah, J., Gong, S., & Ong, E. J. (2001). Face distributions in similarity space under varying head pose. Image and Vision Computing, 19, 807–819. CrossRefGoogle Scholar
  19. Stiefelhagen, R. (2004). Estimating head pose with neural networks results on the Pointing04 ICPR workshop evaluation data. In Proceedings of ICPR workshop visual observation of deictic gestures. Google Scholar
  20. Tu, J., Fu, Y., Hu, Y., & Huang, T. (2007). Evaluation of head pose estimation for studio data. In Proceedings of the 1st international evaluation conference on classification of events, activities and relationships (pp. 281–290). Google Scholar
  21. Voit, M., Nickel, K., & Stiefelhagen, R. (2006). Neural network-based head pose estimation and multi-view fusion. In LNCS. Proceedings of CLEAR workshop (pp. 299–304). Google Scholar
  22. Wang, X., Huang, X., Gao, J., & Yang, R. (2008). Illumination and person-insensitive head pose estimation using distance metric learning. In Proceedings of the 10th European conference on computer vision: part II (pp. 624–637). Google Scholar
  23. Wu, J., & Trivedi, M. M. (2005). An integrated two-stage framework for robust head pose estimation. In IEEE international workshop on analysis and modeling of faces and gestures (pp. 321–335). CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.University of DaytonDaytonUSA

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