Local Binary Patterns and Its Application to Facial Image Analysis: A Survey

By Di Huang, Caifeng Shan, Mohsen Ardabilian, Yunhong Wang, Liming Chen. IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews), 2011

969 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/0596447b7d1c8d72e67ca22a2d5b859021ec509d

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In Computer Vision and Pattern Recognition

Citations in the corpus

DepAudioNet: An Efficient Deep Model for Audio based Depression Classification (2016, methodology): “Recently, Meng et al. [27] applied Local Binary Patterns (LBP) [18] and Edge Orientation Histograms (EOH) to encode the dynamic visual cues represented in the Motion History Histogram (MHH) feature space, and then used Partial Least Square (PLS) to predict depression levels; whereas for audio…”
Local binary features for texture classification: Taxonomy and experimental study (2017, background): “[22] presented a review of the LBP variants in the application area of facial image analysis.”
Median Robust Extended Local Binary Pattern for Texture Classification (2016, background): “Many extensions and modiﬁcations of LBP have been developed with an aim to increase its robustness and discrim-inativeness, with surveys given in [1], [27], and [28].”
Depth-based face recognition using local quantized patterns adapted for range data (2014, background): “For this reason, a wide range of works have been published in the area of computer vision with the aim of recognizing or identifying people faces using different feature extraction techniques and classification solutions [1].”
Texture Feature Extraction Methods: A Survey (2019, methodology): “[96] used LBP-based features in face recognition problems whereas Satpathy et al.”
From BoW to CNN: Two Decades of Texture Representation for Texture Classification (2018, methodology): “…over a broader range of scales), Zhang et al. [264] proposed Local Gabor Binary Pattern (LGBP) by extracting LBP features from images ﬁltered by Gabor ﬁlters of different scales and orientations, to enhance the representation power, followed by subsequent Gabor-LBP approaches [86, 132, 185].”
Image Matching from Handcrafted to Deep Features: A Survey (2020, methodology): “LBP-like methods are widely used in texture representation and face recognition community, and additional details can be found in the review literature (Huang et al. 2011).”