Face Recognition with Local Binary Patterns

By Timo Ahonen, Abdenour Hadid, Matti Pietikäinen. 2004

971 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Xiaogang Wang
3. Dacheng Tao
4. Yi Yang
5. Ming-Hsuan Yang
6. G. Wang
7. Jiwen Lu
8. Trevor Darrell

Citations in the corpus

• Object Detection Networks on Convolutional Feature Maps (2015, methodology): “e Selective Search paper [27] relies on spatial pyramid features [19] on SIFT vectors [22] and an additive kernel SVM. The Regionlet method [30] learns boosted classifiers on a combination of HOG, LBP [2], and covariance features. In these methods, the regions to be classified are either densely enume”
• Face Recognition with Local Binary Patterns (2012, methodology): “Several studies and research work [13], [14], [15] indicates that face recognition using the LBP method provides very good results with di ff erent facial expressions, di ff erent lightening conditions, image rotation and aging of persons.”
• Discriminative multi-manifold analysis for face recognition from a single training sample per person (2011, methodology): “Ten state-of-the-art local feature methods were compared, including LBP [1], LGBP [60], HGGP [57], POEM [42], LDP [56], G-LDP (Gabor-based LDP) [56], RSA (robust and scale approach) [35], GV-LBP [35], and the methods proposed in [61] and [43], respectively.”
• Hierarchical Ensemble of Global and Local Classifiers for Face Recognition (2009, methodology): “Timo et al. [ 10 ] adopted local binary pattern (LBP), which is originated from the area of texture analysis, for face representation.”
• Face recognition using Histograms of Oriented Gradients (2011, background): “There exist a large number of features, starting from the influential Eigenfaces (Principal Component Analysis) (Turk and Pentland, 1991), Gabor wavelets (Amin and Yan, 2009), Local Binary Patterns (Ahonen et al., 2004), Error-Correcting Output Codes (Kittler et al., 2001) and Independent Component ”
• Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition (2005, methodology): “Fisherface 0.94 0.73 0.55 0.31 Best Results of [3] 0.96 0.82 0.59 0.52 Results of [ 10 ] 0.97 0.79 0.66 0.64 Direct LGBPHS 0.94 0.97 0.68 0.53 Weighted LGBPHS 0.98 0.97 0.74 0.71”
• Face detection techniques: a review (2018, methodology): “technique is very effective to describe the image texture features (Ahonen et al. 2004).”
• Boosting Local Binary Pattern (LBP)-Based Face Recognition (2004, methodology): “AdaBoost learning algorithm is used to select the Local Binary Patterns based features [ 14 ], as well as to obtain the similarity function.”
• On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts (2017, background): “of the object contours and local binary pattern (LBP) [10] as texture-based feature.”