Abstract
In the past decade, illumination problem has been the bottleneck of developing robust face recognition systems. Extracting illumination invariant features, especially the gradient based descriptor [13], is an effective tool to solve this issue. In this paper, we propose a novel gradient based descriptor, namely Complete Gradient Face (CGF), to compensate the limitations in [13] and contribute in three folds: (1) we incorporate homogeneous filtering to alleviate the illumination effect and enhance facial information based on the Lambertian assumption; (2) we demonstrate the gradient magnitude in logarithm domain is insensitive to lighting change; (3) we propose a histogram based feature descriptor to integrate both magnitude and orientation information. Experiments on CMU-PIE and Extended YaleB are conducted to verify the effectiveness of our proposed method.
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Zhu, JY., Zheng, WS., Lai, JH. (2012). Complete Gradient Face: A Novel Illumination Invariant Descriptor. In: Zheng, WS., Sun, Z., Wang, Y., Chen, X., Yuen, P.C., Lai, J. (eds) Biometric Recognition. CCBR 2012. Lecture Notes in Computer Science, vol 7701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35136-5_3
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DOI: https://doi.org/10.1007/978-3-642-35136-5_3
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