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

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Abstract

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.

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References

  • 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 

  • 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.

    Article  Google Scholar 

  • 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 

  • Foytik, J., Asari, V., Tompkins, R., & Youssef, M. (2010). Phase space for face pose estimation. In Advances in visual computing (pp. 49–58).

    Chapter  Google Scholar 

  • 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 

  • 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 

  • Gourier, N., Maisonnasse, J., Hall, D., & Crowley, J. L. (2006). Head pose estimation on low resolution images. In CLEAR 2006, South.

    Google Scholar 

  • 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 

  • Kovesi, P. (1999). Image features from phase congruency. In VIDERE journal of computer vision research (Vol. 1).

    Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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.

    Article  Google Scholar 

  • Melzer, T., Reiter, M., & Bischof, H. (2003). Appearance models based on kernel canonical correlation analysis. In Pattern recognition (pp. 1961–1971).

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • Oppenheim, A., & Lim, J. (1981). The importance of phase in signals. Proceedings of the IEEE, 69, 529–541.

    Article  Google Scholar 

  • Sherrah, J., Gong, S., & Ong, E. J. (2001). Face distributions in similarity space under varying head pose. Image and Vision Computing, 19, 807–819.

    Article  Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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).

    Chapter  Google Scholar 

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Authors and Affiliations

  1. University of Dayton, 300 College Park, Dayton, OH, 45469, USA

    Jacob Foytik & Vijayan K. Asari

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  1. Jacob Foytik
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  2. Vijayan K. Asari
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Correspondence to Jacob Foytik.

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Foytik, J., Asari, V.K. A Two-Layer Framework for Piecewise Linear Manifold-Based Head Pose Estimation. Int J Comput Vis 101, 270–287 (2013). https://doi.org/10.1007/s11263-012-0567-y

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  • DOI: https://doi.org/10.1007/s11263-012-0567-y

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