Coupled Kernel Embedding for Low-Resolution Face Image Recognition

By Chuan-Xian Ren, Dao-Qing Dai, Hong Yan. 2012

108 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

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. Samuel F. Dodge
2. Lina Karam
3. Umapada Pal
4. Subhankar Ghosh
5. Saumik Bhattacharya
6. Prasun Roy
7. Santanu Chaudhury
8. Shengwei Zhao

In Signal Processing

1. Shengwei Zhao
2. Chenyu Li
3. Anoop M. Namboodiri
4. Shiming Ge
5. Lina Karam
6. Jia Li

Citations in the corpus

• Effects of Degradations on Deep Neural Network Architectures (2018, methodology): “In [7], the authors used coupled kernel embedding to recognize faces in low resolution images that also suffer from degradations due to high compression.”
• Understanding how image quality affects deep neural networks (2016, background): “There are many works that attempt to recognize low-resolution faces [7], [8].”
• Computer Vision, Graphics, and Image Processing (2016, methodology): “The proposed scheme also outperforms the existing methods [23, 24] in providing robustness, tamper detection and recovery capabilities.”
• Low-Resolution Face Recognition in the Wild via Selective Knowledge Distillation (2018, background): “[12] proposed coupled kernel embedding to map the facial images with different resolutions onto an infinite subspace.”