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Carnegie Mellon Robotics Institute

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Abstract

Pose variations, especially large out-of-plane rotations, make face recognition a difficult problem. In this paper, we propose an algorithm that uses a single input image to accurately synthesize an image of the person in a different pose. We represent the two poses by stacking their information (pixels and feature locations) in a combined feature space. A given test vector will consist of a known part corresponding to the input image and a missing part corresponding to the synthesized image. We then solve for the missing part by maximizing the test vector's probability. This approach combines the ``distance-from-feature-space'' and ``distance-in-feature-space'', and maximizes the test vector's probability by minimizing a weighted sum of these two distances. Our approach does not require either 3D training data or a 3D model, and does not require correspondence between different poses. The algorithm is computationally efficient, and only takes 4 - 5 seconds to generate a face. Experimental results show that our approach produces more accurate results than the commonly used linear-object-class approach. Such technique can help face recognition to overcome the pose variation problem.

Notes

Number of pages: 17

Text Reference

Jiang Ni and Henry Schneiderman, "Face View Synthesis Across Large Angles," in Proceedings of the Second International Workshop on Analysis and Modeling of Faces and Gestures (AMFG 2005) held in conjunction with ICCV 2005, October, 2005.

BibTeX Reference

@inproceedings{Ni_2005_5137,
   author = "Jiang Ni and Henry Schneiderman",
   title = "Face View Synthesis Across Large Angles",
   booktitle = "in Proceedings of the Second International Workshop on Analysis and Modeling of Faces and Gestures (AMFG 2005) held in conjunction with ICCV 2005",
   month = "October",
   year = "2005",
}

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