Wavelet Kernel Construction for Kernel Discriminant Analysis on Face Recognition

2006 ◽  
Author(s):  
Wen-Sheng Chen ◽  
Pong Chi Yuen ◽  
Jian Huang ◽  
Jianghuang Lai
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Kernel Discriminant Analysis

scholarly journals Dual multi-kernel discriminant analysis for color face recognition

Optik ◽  
2017 ◽  
Vol 139 ◽  
pp. 185-201 ◽  
Author(s):  
Qian Liu ◽  
Chao Wang ◽  
Xiao-yuan Jing
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Kernel Discriminant Analysis ◽  
Color Face Recognition

scholarly journals Learning Kernel-Based Robust Disturbance Dictionary for Face Recognition

2019 ◽  
Vol 9 (6) ◽  
pp. 1189 ◽  
Author(s):  
Biwei Ding ◽  
Hua Ji
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Principal Components ◽  
Dictionary Learning ◽  
Recognition Rate ◽  
Computation Time ◽  
Kernel Discriminant Analysis ◽  
Training Samples ◽  
Sparse Representation Classifier ◽  
Facial Changes

In this paper, a kernel-based robust disturbance dictionary (KRDD) is proposed for face recognition that solves the problem in modern dictionary learning in which significant components of signal representation cannot be entirely covered. KRDD can effectively extract the principal components of the kernel by dimensionality reduction. KRDD not only performs well with occluded face data, but is also good at suppressing intraclass variation. KRDD learns the robust disturbance dictionaries by extracting and generating the diversity of comprehensive training samples generated by facial changes. In particular, a basic dictionary, a real disturbance dictionary, and a simulated disturbance dictionary are acquired to represent data from distinct subjects to fully represent commonality and disturbance. Two of the disturbance dictionaries are modeled by learning few kernel principal components of the disturbance changes, and then the corresponding dictionaries are obtained by kernel discriminant analysis (KDA) projection modeling. Finally, extended sparse representation classifier (SRC) is used for classification. In the experimental results, KRDD performance displays great advantages in recognition rate and computation time compared with many of the most advanced dictionary learning methods for face recognition.

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