Gabor-Based Region Covariance Matrices for Face Recognition

By Yanwei Pang, Yuanzhuo Yuan, Xuelong Li. IEEE transactions on circuits and systems for video technology (Print), 2008

299 citations recorded by the source corpus.

How researchers see this paper

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

Sparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approach (2012, unknown): “Covariance descriptors have been exploited in several applications, such as diffusion tensor imaging [4], action recognition [5,6,7], pedestrian detection [3], face recognition [8,9], texture classification [9,10], and tracking [11].”
Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions (2007, unknown): “Output Raw images”
Efficient HOG human detection (2011, background): “Region covariance matrix (RCM) [18] [7] is among the state-of-the-art features for human detection.”
Kernelized covariance for action recognition (2016, methodology): “Other approaches rely on covariance to systematically encode mutual relationships inside the data and such idea was applied to many different applications such as face recognition [24], person identification [25] and more general classification tasks [26].”
Beyond Covariance: Feature Representation with Nonlinear Kernel Matrices (2015, methodology): “For example, considering that Gabor features could extract more important information, they are used to replace the first- and second-order gradients at each pixel to compute covariance matrix in face recognition [15].”
From Manifold to Manifold: Geometry-Aware Dimensionality Reduction for SPD Matrices (2014, methodology): “of a few studies where medium-size matrices were used [15,6].”