Abstract
The performance of face recognition systems are significantly degraded by the pose variations of face images. In this paper, a global pose normalization method is proposed for pose-invariant face recognition. The proposed method uses a deep network to convert non-frontal face images into frontal face images. Unlike existing part-based methods that require complex appearence models or multiple face part detectors, the proposed method relies only on a face detector. The experimental results using the Georgia tech face database demonstrate the advantages of the proposed method.
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Kang, Y., Lee, KT., Eun, J., Park, S.E., Choi, S. (2013). Stacked Denoising Autoencoders for Face Pose Normalization. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_31
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DOI: https://doi.org/10.1007/978-3-642-42051-1_31
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