Abstract
Fully automatic Face Recognition Across Pose (FRAP) is one of the most desirable techniques, however, also one of the most challenging tasks in face recognition field. Matching a pair of face images in different poses can be converted into matching their pixels corresponding to the same semantic facial point. Following this idea, given two images G and P in different poses, we propose a novel method, named Morphable Displacement Field (MDF), to match G with P’s virtual view under G’s pose. By formulating MDF as a convex combination of a number of template displacement fields generated from a 3D face database, our model satisfies both global conformity and local consistency. We further present an approximate but effective solution of the proposed MDF model, named implicit Morphable Displacement Field (iMDF), which synthesizes virtual view implicitly via an MDF by minimizing matching residual. This formulation not only avoids intractable optimization of the high-dimensional displacement field but also facilitates a constrained quadratic optimization. The proposed method can work well even when only 2 facial landmarks are labeled, which makes it especially suitable for fully automatic FRAP system. Extensive evaluations on FERET, PIE and Multi-PIE databases show considerable improvement over state-of-the-art FRAP algorithms in both semi-automatic and fully automatic evaluation protocols.
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References
Zhang, X., Gao, Y.: Face recognition across pose: A review. PR (2009)
Blanz, V., Vetter, T.: Face recognition based on fitting a 3d morphable model. TPAMI (2003)
Asthana, A., Marks, T., Jones, M., Tieu, K., Mv, R.: Fully automatic pose-invariant face recognition via 3d pose normalization. In: ICCV (2011)
Cootes, T., Wheeler, G., Walker, K., Taylor, C.: View-based active appearance models. In: IVC (2002)
Zhang, W., Shan, S., Gao, W., Chen, X., Zhang, H.: Local gabor binary pattern histogram sequence (lgbphs): A novel non-statistical model for face representation and recognition. In: ICCV (2005)
Gross, R., Matthews, I., Baker, S.: Eigen light-fields and face recognition across pose. FG (2002)
Chai, X., Shan, S., Chen, X., Gao, W.: Locally linear regression for pose-invariant face recognition. TIP (2007)
Prince, S., Warrell, J., Elder, J., Felisberti, F.: Tied factor analysis for face recognition across large pose differences. TPAMI (2008)
Ashraf, A., Lucey, S., Chen, T.: Learning patch correspondences for improved viewpoint invariant face recognition. In: CVPR (2008)
Arashloo, S., Kittler, J.: Energy normalization for pose-invariant face recognition based on mrf model image matching. TPAMI (2011)
Castillo, C., Jacobs, D.: Wide-baseline stereo for face recognition with large pose variation. In: CVPR (2011)
Phillips, P., Moon, H., Rizvi, S., Rauss, P.: The feret evaluation methodology for face-recognition algorithms. TPAMI (2000)
Sim, T., Baker, S., Bsat, M.: The cmu pose, illumination, and expression (pie) database. FG (2002)
Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-pie. In: IVC (2010)
Su, Y., Shan, S., Chen, X., Gao, W.: Hierarchical ensemble of gabor fisher classifier for face recognition. FG (2006)
Baocai, Y., Yanfeng, S., Chengzhang, W., Yun, G.: Bjut-3d large scale 3d face database and information processing. JCRD (2009)
Vetter, T., Poggio, T.: Linear object classes and image synthesis from a single example image. TPAMI (1997)
Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. IJCAI (1981)
Shewchuk, J.: An introduction to the conjugate gradient method without the agonizing pain. In: CMUCS-TR (1994)
Li, A., Shan, S., Chen, X., Gao, W.: Maximizing intra-individual correlations for face recognition across pose differences. In: CVPR (2009)
Gross, R., Matthews, I., Baker, S.: Generic vs. person specific active appearance models. In: IVC (2005)
Murphy-Chutorian, E., Doshi, A., Trivedi, M.: Head pose estimation for driver assistance systems: A robust algorithm and experimental evaluation. In: ITSC (2007)
Zhao, X., Chai, X., Niu, Z., Heng, C., Shan, S.: Context constrained facial landmark localization based on discontinuous haar-like feature. FG (2011)
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© 2012 Springer-Verlag Berlin Heidelberg
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Li, S., Liu, X., Chai, X., Zhang, H., Lao, S., Shan, S. (2012). Morphable Displacement Field Based Image Matching for Face Recognition across Pose. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33718-5_8
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DOI: https://doi.org/10.1007/978-3-642-33718-5_8
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