Robust sparse coding for face recognition

By Meng Yang, Lei Zhang, Jian Yang, David Dapeng Zhang. 2011

624 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

How researchers see this paper

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

In Computational Mechanics

1. David Dapeng Zhang
2. Lei Zhang
3. Xiangchu Feng
4. Jian Yang
5. Zeyuan Allen-Zhu
6. Yong Xu
7. Xuelong Li
8. Meng Yang

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Lei Zhang
3. Luc Van Gool
4. Ling Shao
5. Ming-Hsuan Yang
6. Xiaogang Wang
7. Yi Yang
8. Wangmeng Zuo

Citations in the corpus

• Feature Purification: How Adversarial Training Performs Robust Deep Learning (2020, methodology): “We use a data model called sparse coding, which is a popular model to model image, text and speech data [11, 12, 69, 77, 78, 100, 105, 106].”
• Sparse coding based visual tracking: Review and experimental comparison (2013, methodology): “Recently, a robust sparse representation model was proposed in Yang et al. [56], which removes the occlusion dictionary from the sparse representation and uses a weighted LASSO algorithm to handle occlusion.”
• Half-Quadratic-Based Iterative Minimization for Robust Sparse Representation (2014, methodology): “…additive form of HQ, we propose an ‘1-regularized error correction method by iteratively recovering corrupted data from errors incurred by noises and outliers; by using the multiplicative form of HQ, we propose an ‘1-regularized error detection method by learning from uncorrupted data iteratively.”
• A Survey of Sparse Representation: Algorithms and Applications (2015, methodology): “[189] developed a new robust sparse coding (RSC) algorithm, which first obtained a sparsity-constrain ed regression model based on maximum likelihood estimation an d exploited an iteratively reweighted regularized robust co ding algorithm to solve the pre-proposed model.”
• Nuclear Norm Based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes (2014, methodology): “However, in many cases of occlusion, this assumption does not hold.”
• Capped Lp-Norm Graph Embedding for Photo Clustering (2016, methodology): “Models using this norm have demonstrated effective performance [21].”
• 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 Based Fisher Discrimination Dictionary Learning for Image Classification (2014, background): “…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 al.…”
• Latent semantic sparse hashing for cross-modal similarity search (2014, methodology): “Image Sparse Coding has been popularly used as an effective image representation in many applications, such as image classification [31], face recognition [32], image denoising [5] and image restoration [20].”
• Transfer Sparse Coding for Robust Image Representation (2013, background): “However, when labeled and unlabeled images are sampled from different distributions, they may be quantized into different visual words of the codebook and encoded with different representations.”
• Ensemble Sparse Classification of Alzheimer’s Disease (2012, background): “It also boosts the research on the sparsity based pattern classification (Majumdar and Ward, 2009; Yang et al., 2011).”