Maximum Correntropy Criterion for Robust Face Recognition

By Ran He, Weishi Zheng, Bao-Gang Hu. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011

665 citations recorded by the source corpus.

Subfields: Computational Mechanics, Electrical and Electronic Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/332d773b70f2f6fb725d49f314f57b8f8349a067

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. Dacheng Tao
2. Tongliang Liu
3. Ran He
4. Liang Wang
5. Kaiye Wang
6. Jian Yang
7. Tien-Ping Tan
8. Weishi Zheng

In Electrical and Electronic Engineering

1. Liang Wang
2. Kaiye Wang
3. Tien-Ping Tan
4. Ran He
5. Tongliang Liu
6. Dacheng Tao
7. Jian Yang
8. Bao-Gang Hu

In Signal Processing

1. Ran He
2. Tien-Ping Tan
3. Zhenan Sun
4. Tongliang Liu
5. Weishi Zheng
6. Bao-Gang Hu
7. Dacheng Tao
8. Liang Wang

Citations in the corpus

• A Comprehensive Survey on Cross-modal Retrieval (2016, methodology): “…CVH [60], IMH [61], PDH [62], LCMH [63], CMFH [64], LSSH [65] Nonlinear modeling MSAE [66], DMHOR [67] Pairwise based methods Linear modeling CMSSH [68], CRH [69], IMVH [70], QCH[71] RaHH [72], HTH [73] Nonlinear modeling MLBE [74], PLMH [75], MM-NN [76], CHN [77] Supervised methods Linear…”
• Ieee Transactions on Pattern Analysis and Machine Intelligence Classification with Noisy Labels by Importance Reweighting (positive): “Robust surrogate loss functions, such as the Cauchy loss function [29] and correntropy (also known as the Welsch loss function), [30], [31], have been empirically proven to be robust to noise.”
• Half-Quadratic-Based Iterative Minimization for Robust Sparse Representation (2014, background): “Although several robust sparse models have been proposed and promising results have been obtained, they are either for error correction or for error detection, and learning a general framework that systematically unifies these two aspects and explores their relation is still an open problem.”
• Robust support vector machines based on the rescaled hinge loss function (2017, methodology): “For example, under the framework of sparse representation classifier [22], the sparse representation classifiers based on Correntropy are proposed for robust face recognition in [23,24].”
• Robust Principal Component Analysis Based on Maximum Correntropy Criterion (2011, background): “Proposition 1: There exists a convex conjugated function of such that (11) where is a scalar variable, and, for a fixed , the maximum is reached at , [25], [27].”
• A Survey of Sparse Representation: Algorithms and Applications (2015, methodology): “[188] proposed utilizing the maximum correntropy criterion name d CESR embedding non-negative constraint and half-quadrati c optimization to present a robust face recognition algorith m.”
• Nuclear Norm Based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes (2014, methodology): “Towards this end, M. Yang et al. borrowed the idea robust regression [5] and proposed a regularized robust coding method [6, 7].”
• Partial Person Re-Identification (2015, positive): “Recently, sparse representation or dictionary learning has been utilised for solving the occlusion problem in face recognition [32, 43, 6, 35].”
• Learning Coupled Feature Spaces for Cross-Modal Matching (2013, methodology): “Here, an iterative algorithm based on the half-quadratic minimization [8, 9] is proposed to solve this problem.”
• Joint Feature Selection and Subspace Learning for Cross-Modal Retrieval (2016, methodology): “In this subsection, an iterative algorithm based on the halfquadratic minimization [9], [52] is proposed to solve the objective function in (4).”
• Deep Audio-visual Learning: A Survey (2020, methodology): “Prominent achievements include speech recognition [1, 2] , facial recognition [3, 4] , etc. Audio-visual learning (AVL) using both modalities has been introduced to overcome the limitation of perception tasks in each modality.”