Linear Regression for Face Recognition

By Imran Naseem, Roberto B. Togneri, Mohammed Bennamoun. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010

1,012 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/7d3f6dd220bec883a44596ddec9b1f0ed4f6aca2

How researchers see this paper

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

In Computer Vision and Pattern Recognition

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

In Signal Processing

1. Julien Mairal
2. Francis R. Bach
3. Ran He
4. Tien-Ping Tan
5. Jean Ponce
6. Zhenan Sun
7. Jian Yang
8. Weishi Zheng

Citations in the corpus

• Deep Reconstruction Models for Image Set Classification (2015, methodology): “Next, during the testing phase of pose group approximation, Sg is used for a linear regression based classification strategy [40].”
• Regression Networks for Meta-Learning Few-Shot Classification (2019, methodology): “For example, the linear regression classification (LRC) method (Naseem et al., 2010) relies on the fact that the set of all reflectance functions produced by Lambertian objects, which parts of natural images are composed of, lie near a low-dimensional vector subspace (Basri and Jacobs, 2003).”
• Half-Quadratic-Based Iterative Minimization for Robust Sparse Representation (2014, methodology): “In addition, HQSA and HQSM perform slightly better than LRC and CRC. Fig.”
• Nuclear Norm Based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes (2014, methodology): “Actually, the previous works, like the nearest feature line [2], the nearest feature plane and the nearest feature space methods [3] are all variants of LR based methods.”
• Synthesized Feature based Few-Shot Class-Incremental Learning on a Mixture of Subspaces (2021, methodology): “Modeling data by projection onto subspaces has been widely used in many computer vision and machine learning applications [22, 1, 17, 16, 32, 23].”
• Maximum Correntropy Criterion for Robust Face Recognition (2011, methodology): “To extract robust facial features, a number of algorithms, such as modular Eigenspaces [4], modular linear regression based classification (LRC) [5], eigenimages [6], [7], [8], minimax probability machine [9], reconstructive and discriminative subspace method [10], and associative memories [11],…”
• A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic (2020, positive): “The used Ensemble methods are K-Nearest Neighbors Algorithm (k-NN) [28], Linear Regression [29] and Logistic Regression [30].”
• Sparse Modeling for Image and Vision Processing (2014, methodology): “, 2009], or methods looking for the nearest subspace [Naseem et al., 2010], which project the data x onto each span of the submatrices Dj , before selecting the subspace that best reconstructs the input data.”
• A Survey of Sparse Representation: Algorithms and Applications (2015, methodology): “Thus, in terms of the diff erent norms used in optimizers, the sparse representation method s can be roughly grouped into five general categories: sparse representation with the l0-norm minimization [36, 37], sparse representation with thelp-norm (0<p<1) minimization [38– 40], sparse representation ”