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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Face Recognition from Visible and Near-Infrared Images Using Boosted Directional Binary Code
verfasst von : Linlin Shen, Jinwen He, Shipei Wu, Songhao Zheng
Erschienen in: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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Pose and illuminations remain great challenges to current face recognition technique. In this paper, visible image (VI) and near-infrared image (NIR) are fused for performance improvement. When directional binary code is adopted as feature representation, AdaBoost algorithm and the cascade structure are used for classification. Fusion is done at decision level and classification scores are normalized using three different rules, i.e. Min-Max, Z-Score and Tanh-Estimators. Experimental results suggest that the proposed algorithm using VI achieve better performance than NIR when pose and expression variations are present. However, NIR shows much better robustness against illumination and time difference than VI. Due to the complementary information available in two image modalities, fusion of NIR and VI further improves the system performance.