(2D)2PCA: Two-directional two-dimensional PCA for efficient face representation and recognition

By Daoqiang Zhang, Zhi-Hua Zhou. Neurocomputing, 2005

645 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/68d7be102d8b298d172d120d5a7ae1bfd5b8ffb9

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. Dacheng Tao
2. Jiwen Lu
3. Xuelong Li
4. G. Wang
5. Ling Shao
6. Jie Li
7. Xinbo Gao
8. Yi Yang

In Media Technology

1. Dacheng Tao
2. Xuelong Li
3. Xinbo Gao
4. N. Wang
5. Rong Chen
6. Jianqing Fan
7. Yap-Peng Tan
8. Jiwen Lu

Citations in the corpus

• Statistical Inference for High-Dimensional Matrix-Variate Factor Models (2020, methodology): “In the community of image signal processing, model (1.1) and estimation methods such as (2 D ) 2 -PCA have been actively studied (Yang et al., 2004; Zhang and Zhou, 2005; Kong et al., 2005; Pang et al., 2008; Kwak, 2008; Li et al., 2010; Meng et al., 2012; Wang et al., 2015).”
• A Comprehensive Survey to Face Hallucination (2013, methodology): “Kumar and Aravind (2008a) also proposed a similar idea that combined 2D-PCA (Zhang and Zhou 2005) and KRR to hallucinate input lowresolution images, in which 2D-PCA was explored to estimate the 2D-PCA projection features (coefficients) of the high-resolution image and KRR was applied to estimate…”
• Factor models for matrix-valued high-dimensional time series (2016, methodology): “Similar models as model (1) have been proposed and studied when conducting principal component analysis on matrix-valued data (e.g. Paatero and Tapper, 1994; Yang et al., 2004; Ye, 2005; Ding and Ye, 2005; Zhang and Zhou, 2005; Crainiceanu et al., 2011; Wang et al., 2016).”
• Discriminative multi-manifold analysis for face recognition from a single training sample per person (2011, methodology): “This is because, compared with holistic-based methods such as PCA, 2DPCA, LPP, and UP, the proposed DMMA extracts features from local patches and the appearance differences caused by aging are mitigated in the local patches and better age-invariant feature can be preserved.”