Hierarchical Ensemble of Global and Local Classifiers for Face Recognition

By Yu Su, Shiguang Shan, Xilin Chen, Wen Gao. IEEE Transactions on Image Processing, 2009

371 citations recorded by the source corpus.

How researchers see this paper

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

Face recognition based on non-corresponding region matching (2011, methodology): “It should be pointed that our method and the approach in [12] only use the Gabor feature, while methods in [19, 22, 4] integrating Gabor features with other kind of visual features.”
Gabor Feature Based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary (2010, background): “Automatic face recognition (FR) is one of the most visible and challenging research topics in computer vision, machine learning and biometrics [1], [2], [3].”
Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition (2014, background): “MDML-DCPs outperforms [33], which also employs the PLDAbased classifier, by over 5%.”
The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification (2017, methodology): “This structure could mimic the cascading/hierarchical ensemble [Wang et al., 2014, Su et al., 2009].”