Gabor Feature Based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary

By Meng Yang, Lei Zhang. European Conference on Computer Vision, 2010

481 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/cea50611ba73b5775cc2fe1e9c27990a0bb20cf8

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In Computational Mechanics

In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Fisher Discrimination Dictionary Learning for sparse representation (2011, background): “Recently sparse representation techniques have also led to promising results in image classification, e.g. face recognition (FR) [5] [6] [7] [10] [31], digit and texture classification [8] [9] [11] [12], etc.”
Sparse representation or collaborative representation: Which helps face recognition? (2011, methodology): “Lei Zhang a , Meng Yang a , and Xiangchu Feng b a Dept. of Computing, The Hong Kong Polytechnic University, Hong Kong, China b Dept. of Applied Mathematics, Xidian University, Xi'an, China {cslzhang, csmyang}@comp.polyu.edu.hk powerful for FR?”
Sparse coding based visual tracking: Review and experimental comparison (2013, background): “To overcome them, a learned occlusion dictionary based on Gabor features was proposed for face recognition [48].”
Sparse Representation Based Fisher Discrimination Dictionary Learning for Image Classification (2014, background): “Recently sparse representation techniques have also led to promising results in face recognition (Wright et al. 2009b; Wang et al. 2009; Yang and Zhang 2010; Yang et al. 2011a,b), handwritten digit and texture classification (Huang and Aviyente 2006; Mairal et al. 2009; Ramirez et al. 2010; Yang et…”
Sparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approach (2012, methodology): “Lastly, we show that the proposed sparse coding approach obtains superior performance on several visual classification tasks (face recognition, texture classification, person re-identification), in comparison to several state-of-the-art methods: tensor sparse coding [9], log-Euclidean sparse represe”
Robust sparse coding for face recognition (2011, methodology): “Compared to GSRC, over 6% improvement is achieved by RSC for scarf disguise.”
From Manifold to Manifold: Geometry-Aware Dimensionality Reduction for SPD Matrices (2014, methodology): “In addition to our algorithms, we evaluated the state-of-the-art Sparse Representation based Classification (SRC) [24] and its Gabor-based extension (GSRC) [26].”
Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-free Approach (2018, background): “Further studies [4, 27, 24, 23] on face recognition with SRC have also been conducted.”
Recognizing Disguised Faces: Human and Machine Evaluation (2014, background): “Yang and Zhang [55] Gabor SRC No Yes Visible AR, Yale B”
Metaface learning for sparse representation based face recognition (2010, background): “Compute the residuals 1 2 ˆ , for 1, , .i ir A i ky y where : n ni is the characteristic function which selects the coefficients associated with the i-th class.”
Deeply learned face representations are sparse, selective, and robust (2014, background): “Sparse representation-based classification [33, 34, 35, 8] was extensively studied for face recognition with occlusions.”