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Face Sketch Synthesis Using Regularized Broad Learning System
Li, Ping1; Sheng, Bin2; Chen, C. L.P.3
2022-10
Source PublicationIEEE Transactions on Neural Networks and Learning Systems
ISSN2162-237X
Volume33Issue:10Pages:5346-5360
Abstract

There are two main categories of face sketch synthesis: data- and model-driven. The data-driven method synthesizes sketches from training photograph-sketch patches at the cost of detail loss. The model-driven method can preserve more details, but the mapping from photographs to sketches is a time-consuming training process, especially when the deep structures require to be refined. We propose a face sketch synthesis method via regularized broad learning system (RBLS). The broad learning-based system directly transforms photographs into sketches with rich details preserved. Also, the incremental learning scheme of broad learning system (BLS) ensures that our method easily increases feature mappings and remodels the network without retraining when the extracted feature mapping nodes are not sufficient. Besides, a Bayesian estimation-based regularization is introduced with the BLS to aid further feature selection and improve the generalization ability and robustness. Various experiments on the CUHK student data set and Aleix Robert (AR) data set demonstrated the effectiveness and efficiency of our RBLS method. Unlike existing methods, our method synthesizes high-quality face sketches much efficiently and greatly reduces computational complexity both in the training and test processes.

KeywordBayesian Estimation Computational Modeling Face Recognition Face Sketch Synthesis Faces Feature Extraction Incremental Learning Learning Systems Regularized Broad Learning System (Rbls). Robustness Training
DOI10.1109/TNNLS.2021.3070463
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000732186000001
Scopus ID2-s2.0-85104243272
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Cited Times [WOS]:5   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Department of Computing, The Hong Kong Polytechnic University, Hong Kong.
2.Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China (e-mail: shengbin@sjtu.edu.cn)
3.School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China, also with the Navigation College, Dalian Maritime University, Dalian 116026, China, and also with the Faculty of Science and Technology, University of Macau, Macau 999078, China.
Recommended Citation
GB/T 7714
Li, Ping,Sheng, Bin,Chen, C. L.P.. Face Sketch Synthesis Using Regularized Broad Learning System[J]. IEEE Transactions on Neural Networks and Learning Systems,2022,33(10):5346-5360.
APA Li, Ping,Sheng, Bin,&Chen, C. L.P..(2022).Face Sketch Synthesis Using Regularized Broad Learning System.IEEE Transactions on Neural Networks and Learning Systems,33(10),5346-5360.
MLA Li, Ping,et al."Face Sketch Synthesis Using Regularized Broad Learning System".IEEE Transactions on Neural Networks and Learning Systems 33.10(2022):5346-5360.
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