Face Recognition with Local Binary Patterns

By Ammad Ali, Shah Hussain, Farah Haroon, Sajid Hussain, M. F. Khan. 2012

1,947 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/15885415819f2a9db8eee46ea6b6224c759801fa

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Citations in the corpus

Learning local binary patterns for gender classification on real-world face images (2012, methodology): “In the existing studies (Ahonen et al., 2004), to consider the shape information, face images are divided into non-overlapping sub-regions (as shown in Fig.”
Low-Resolution Face Recognition in the Wild via Selective Knowledge Distillation (2018, background): “Recently, the general face recognition technique has evolved from the classic shallow frameworks [13], [14] to deep ones [1], [3], [4], [15]–[18] with impressive performance improvements.”
Face recognition with learning-based descriptor (2010, methodology): “’s work [1], the encoded image is divided into a grid of patches (5×7 patches for the holistic face (84×96) used in this section).”
Histogram of Oriented Normal Vectors for Object Recognition with a Depth Sensor (2012, background): “Local texture based features such as Local binary pattern (LBP) is also widely used as complementary information to gradients [20, 21].”
A Survey of Recent Advances in Face Detection (2010, background): “Another well-known feature set robust to illumination variations is the local binary patterns (LBP) [65], which have been very effective for face recognition tasks [2, 117].”
Secure Face Matching Using Fully Homomorphic Encryption (2018, background): “• Add(ct0, ct1): Output (ct0[0]+ct1[0], ct0[1]+ct1[1])”
Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition (2011, methodology): “Specifically, we separately used Multi-Region Histograms (MRH) [28] and Local Binary Patterns (LBP) [4] to extract features from each face.”
Regionlets for Generic Object Detection (2013, background): “In object detection, an object category is essentially defined by a classifier where both object appearance and the spatial layout inside an object shall be taken into account.”
When Face Recognition Meets with Deep Learning: An Evaluation of Convolutional Neural Networks for Face Recognition (2015, background): “In constrained environments, hand-crafted features such as Local Binary Patterns (LBP) [1] and Local Phase Quantisation (LPQ) [2, 4] have achieved respectable face recognition performance.”
Feature Learning for Image Classification Via Multiobjective Genetic Programming (2014, unknown): “2.”
A Completed Modeling of Local Binary Pattern Operator for Texture Classification (2010, unknown): “LBP has also been adapted to many other applications, such as face recognition [15], dynamic texture recognition [16] and shape localization [17].”
Improving object detection with deep convolutional networks via Bayesian optimization and structured prediction (2015, methodology): “These methods use image descriptors such as HOG [8], SIFT [30], and LBP [1] as features and densely sweep through the entire image to find a maximum response region.”
Probabilistic Elastic Matching for Pose Variant Face Verification (2013, background): “Face recognition has remained an active research topic in computer vision for decades [17, 32, 31, 22, 2, 11, 1, 21, 30, 14, 18, 29, 7].”
Learning Deep Face Representation (2014, background): “Despite the progress in this direction, the current state-of-the-art system on the LFW benchmark is built upon the hand-crafted LBP descriptor [1] which was proposed more than a decade ago.”
Counter-measures to photo attacks in face recognition: A public database and a baseline (2011, methodology): “In this particular setup, faces are detected using a cascade of classifiers based on a variant of Local Binary Patterns (LBP) [21] referred as Modified Census Transform (MCT) [22].”
Privileged Information for Modeling Affect In The Wild (2021, methodology): “Popular visual descriptors, such as SIFT [12], HOG [13] and LBP [14] have been widely used to represent human facial patterns via a set of features used as inputs to machine learning models for emotion recognition [15], [16].”