Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition

By Wenchao Zhang, Shiguang Shan, Wen Gao, Xilin Chen, Hongming Zhang. Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005

1,124 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a7e1b5c86f6d2282e009aa9040806d573d192801

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In Computer Vision and Pattern Recognition

Citations in the corpus

KinectFaceDB: A Kinect Database for Face Recognition (2014, methodology): “Tables VIII and IX illustrate the fusion results from both the RGB and depth using PCA, LBP, and LGBP (SIFT is not used because it cannot capture the correct information from depth images as shown in Section IV-D2).”
A Comparative Study of Local Matching Approach for Face Recognition (2007, methodology): “Zhang et al. proposed local Gabor binary pattern histogram sequence (LGBPHS) by combining Gabor filters and the local binary operator [53].”
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.”
Learning Discriminant Face Descriptor (2014, methodology): “A lot of work has been proposed in this branch [20], [21], [22].”
Gabor Feature Based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary (2010, background): “Automatic face recognition (FR) is one of the most visible and challenging research topics in computer vision, machine learning and biometrics [1], [2], [3].”
Cosine Similarity Metric Learning for Face Verification (2010, background): “Features can be combined at the feature extraction step [18,19] or at the veri cation step.”
Learning Compact Binary Face Descriptor for Face Recognition (2015, background): “The following describes the details of the experiments and results.1”
Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions (2007, methodology): “This is in line with the state of the art – the best method tested in [12], LGBPHS [52], scored slightly more than 50%, while pixel based eigenfaces and fisherfaces respectively scored only 8.2% and 21.8% – but it is not very satisfying in absolute terms.”
Deep Learning Identity-Preserving Face Space (2013, methodology): “In this setting, we show superior results in Table 1, where the FIP and RL features are compared with four methods, including LGBP [34], VAAM [3], FA-EGFC [18], and SAEGFC [18], and two learning-based descriptors, including LE [4] and CRBM [10].”
Local Transform Features and Hybridization for Accurate Face and Human Detection (2013, background): “Index Terms—Local binary pattern, local gradient pattern, binary histograms of oriented gradients, feature hybridization, face and human detection Ç”
Local binary features for texture classification: Taxonomy and experimental study (2017, methodology): “Ahonen et al. [93] proposed LBP (LBPHF) to achieve rotation invariance globally by combining LBP and Discrete Fourier transform (DFT).”
Fusing Gabor and LBP Feature Sets for Kernel-Based Face Recognition (2007, background): “Both representations are rich in information and computationally efﬁcient.”