Abstract
Large pose and illumination variations are very challenging for face recognition. In this paper, we address this challenge by combining an Adaptive Quotient Image method with 3D Generic Elastic Models (AQI-GEM). Frontal, neutral light face is re-rendered virtually under varying illumination conditions by AQI. Nearly accurate 3D models are constructed from each re-rendered image by GEM so as to virtually synthesize images under varying poses and illumination conditions. Pose-specific metrics are learnt for recognition. Experiments on MultiPIE demonstrate that it outperforms state-of-the-art face recognition methods, with much simpler parameter tuning, and much less training data.
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Wu, Z., Deng, W. (2015). Adaptive Quotient Image with 3D Generic Elastic Models for Pose and Illumination Invariant Face Recognition. In: Yang, J., Yang, J., Sun, Z., Shan, S., Zheng, W., Feng, J. (eds) Biometric Recognition. CCBR 2015. Lecture Notes in Computer Science(), vol 9428. Springer, Cham. https://doi.org/10.1007/978-3-319-25417-3_1
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DOI: https://doi.org/10.1007/978-3-319-25417-3_1
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