Face Description with Local Binary Patterns: Application to Face Recognition

By Timo Ahonen, Abdenour Hadid, Matti Pietikäinen. 2006

5,956 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

The MegaFace Benchmark: 1 Million Faces for Recognition at Scale (2015, methodology): “Participating algorithms In addition to baseline algorithms LBP, and Joint Bayes, we present results of the following methods (some provided more than 1 model): 1.”
Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis (2017, methodology): “For the ﬁrst category, traditional methods often make use of robust local descriptors such as Gabor [5], Haar [35] and LBP [2] to account for local distortions and then adopt metric learning [4,36] techniques to achieve pose invariance.”
Surpassing Human-Level Face Verification Performance on LFW with GaussianFace (2014, methodology): “2) Modern face veriﬁcation methods are mainly divided into two categories: extracting low-level features [36, 3, 34, 10, 24], and building classiﬁcation models [62, 50, 13, 37, 31, 56, 5, 28, 47, 33].”
Oriented Response Networks (2017, methodology): “Rotation invariance is achieved by minimizing the LBP code value using the bit cyclic shift.”
Discriminative Deep Metric Learning for Face Verification in the Wild (2014, methodology): “For the LFW-a dataset, we crop each image into 80 × 150 to remove the background information, and then extract two features: Dense SIFT (DSIFT) [22] and LBP [1].”
Frontal to profile face verification in the wild (2016, methodology): “Also HoG and LBP features used in the baseline, need dense sets of 30 facial key-point to extract patches, whereas Fisher Vector only needs 3 key-points for rough alignment.”
Robust Facial Expression Recognition Based on Local Directional Pattern (2010, background): “However, for face images, some degree of locations and spatial relationship represents the image content better [35], [36].”
Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments (2017, methodology): “We apply Local Binary Patterns (LBP) [1] as feature for the comparison on metric learning approaches.”
Pairwise Rotation Invariant Co-Occurrence Local Binary Pattern (2014, background): “…role in a wide range of applications that includes static and dynamic texture classification [1], [2], [3], [4], [5], object and scene recognition [6], [7], [8], [9], [10], face detection and recognition [11], image retrieval [12], [13], stereo correspondence, 3D reconstruction and many more.”
Face recognition in unconstrained videos with matched background similarity (2011, background): “Local Binary Patterns (LBP) encoding [1].”
Facilitating fashion camouflage art (2013, background): “The most popular local features used in face recognition systems are Local Binary Pattern [23] and Gabor Features [24].”
Deep Face Recognition: A Survey (2018, background): “In the early 2000s, this problem gave rise to local-feature-based FR. Gabor [104] and LBP [5], as well as their multilevel and high-dimensional extensions [222][26][41], achieved robust performance through some invariant properties of local filtering.”
Robust Face Recognition via Multimodal Deep Face Representation (2015, methodology): “Among the handcrafted descriptors, Ahonen et al. [3] proposed to employ the texture descriptor LBP for face representation.”
Recover Canonical-View Faces in the Wild with Deep Neural Networks (2014, methodology): “The high dimensional concatenations of the local descriptors , such as Haar [35], Gabor [15], and LBP [1], have demonstrated their robustness to local distortions and achieved significant improvement in”