Abstract
Face recognition is an active research area due to its wide range of practical applications. Efficient and discriminative facial feature is a crucial issue for face recognition. Most existing methods use one type of features but we show that robust face recognition requires different kinds of feature information to be taken into account. Traditional feature fusion methods are based on the linear combination. In this study, we propose a novel and effective fusion method (called NF-PCF), which uses polynomial correlation filter (PCF) to non-linearly fuse different types of features for robust face recognition. Experimental results on two popular face databases, including Yale and PIE, show the promising results obtained by the proposed method.
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Yan, D., Shen, Y., Yan, Y., Wang, H. (2013). Non-linear Feature Fusion Based on Polynomial Correlation Filter for Face Recognition. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_40
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DOI: https://doi.org/10.1007/978-3-642-42057-3_40
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