Learning Kernel-Based Robust Disturbance Dictionary for Face Recognition
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...
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doaj-023a2a4fa88e4d4797fdd99c40a839f52020-11-25T00:50:21ZengMDPI AGApplied Sciences2076-34172019-03-0196118910.3390/app9061189app9061189Learning Kernel-Based Robust Disturbance Dictionary for Face RecognitionBiwei Ding0Hua Ji1School of Information Science and Engineering, Shandong Normal University, Jinan 250358, ChinaSchool of Information Science and Engineering, Shandong Normal University, Jinan 250358, ChinaIn 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.https://www.mdpi.com/2076-3417/9/6/1189face recognitiondictionary learningkernel discriminant analysis (KDA)sparse representation classifier (SRC) |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Biwei Ding Hua Ji |
spellingShingle |
Biwei Ding Hua Ji Learning Kernel-Based Robust Disturbance Dictionary for Face Recognition Applied Sciences face recognition dictionary learning kernel discriminant analysis (KDA) sparse representation classifier (SRC) |
author_facet |
Biwei Ding Hua Ji |
author_sort |
Biwei Ding |
title |
Learning Kernel-Based Robust Disturbance Dictionary for Face Recognition |
title_short |
Learning Kernel-Based Robust Disturbance Dictionary for Face Recognition |
title_full |
Learning Kernel-Based Robust Disturbance Dictionary for Face Recognition |
title_fullStr |
Learning Kernel-Based Robust Disturbance Dictionary for Face Recognition |
title_full_unstemmed |
Learning Kernel-Based Robust Disturbance Dictionary for Face Recognition |
title_sort |
learning kernel-based robust disturbance dictionary for face recognition |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2019-03-01 |
description |
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. |
topic |
face recognition dictionary learning kernel discriminant analysis (KDA) sparse representation classifier (SRC) |
url |
https://www.mdpi.com/2076-3417/9/6/1189 |
work_keys_str_mv |
AT biweiding learningkernelbasedrobustdisturbancedictionaryforfacerecognition AT huaji learningkernelbasedrobustdisturbancedictionaryforfacerecognition |
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