Abstract
In this paper, we propose a new local descriptor named as PCA-LBP for face recognition. In contrast to classical LBP methods, which compare pixels about single value of intensity, our proposed method considers that comparison among image patches about their multi-dimensional subspace representations. Such a representation of a given image patch can be defined as a set of coordinates by its projection into a subspace, whose basis vectors are learned in selective facial image patches of the training set by Principal Component Analysis. Based on that, PCA-LBP descriptor can be computed by applying several LBP operators between the central image patch and its 8 neighbors considering their representations along each discretized subspace basis. In addition, we propose PCA-CoALBP by introducing co-occurrence of adjacent patterns, aiming to incorporate more spatial information. The effectiveness of our proposed two methods is accessed through evaluation experiments on two public face databases.
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Zong, X. (2018). Local Binary Patterns Based on Subspace Representation of Image Patch for Face Recognition. In: Bai, X., Hancock, E., Ho, T., Wilson, R., Biggio, B., Robles-Kelly, A. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2018. Lecture Notes in Computer Science(), vol 11004. Springer, Cham. https://doi.org/10.1007/978-3-319-97785-0_13
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