Histogram of Gabor Phase Patterns (HGPP): A Novel Object Representation Approach for Face Recognition

By Baochang Zhang, Shiguang Shan, Xilin Chen, Wen Gao. IEEE Transactions on Image Processing, 2007

647 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/0f453074b457ad22d9c02ba033a8fd7a0ddbda92

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In Computer Vision and Pattern Recognition

Citations in the corpus

Gabor Convolutional Networks (2017, background): “The Gabor wavelets (kernels or filters) are defined as follows [30, 33]:”
Unconstrained face verification using deep CNN features (2015, background): “Gabor wavelets [33][31] have also been widely used to encode multi-scale and multi-orientation information for face images.”
Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition (2014, background): “Index Terms—Face recognition, face image descriptors, face image representation F”
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.”
Median Robust Extended Local Binary Pattern for Texture Classification (2016, methodology): “Thus the methods are used in a sparse approach, like that of Lazebnik et al. [39] and Zhang et al. [40], where salient regions are described with multiple descriptors such as SIFT, RIFT and SPIN.”
Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor (2010, methodology): “ture descriptors extracted by a set of Gabor wavelets (kernels) which model the receptive field profiles of cortical simple cells [5], [6], [15], [28], [29].”
Local Directional Number Pattern for Face Analysis: Face and Expression Recognition (2013, methodology): “This approach allows us to distinguish intensity changes ( e.g., from bright to dark and vice versa) in the texture, that otherwise will be missed— see Fig.”
Learning Discriminant Face Descriptor (2014, methodology): “A lot of work has been proposed in this branch [20], [21], [22].”
Learning Compact Binary Face Descriptor for Face Recognition (2015, neutral): “0 HGGP [69] 97.”
Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions (2007, neutral): “Output Raw images”
Local binary features for texture classification: Taxonomy and experimental study (2017, methodology): “We use the original source code if it is publicly available, and for the remainder we have developed our own implementation.”
Face Recognition Under Varying Illumination Using Gradientfaces (2009, methodology): “In fact, the “common” assumption is a widely accepted assumption which has been used in [16]–[18], [22], [25].”