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Fast and accurate face detection by sparse Bayesian extreme learning machine
Vong, C. M.; Tai, K.I.; Pun, C. M.; Wong, P. K.
2015-07-01
Source PublicationNeural Computing and Applications (SCI-E)
ISSN0941-0643
Pages1149-1156
AbstractReal-time face detection is an important research topic in computer vision and pattern recognition. One of the effective methods in face detection is model- based approach which employs neural network technique for the construction of classification model. Relevant techniques such as support vector machines are fast in training an accurate model which is, however, relatively slow in execution time. The reason is due to the large size of the constructed model. In this paper, the main contri- bution is to apply a new method called sparse Bayesian extreme learning machine (SBELM) for real-time face detection because SBELM can minimize the model size with nearly no compromise on the accuracy and have fast execution time. Several benchmark face datasets were employed for the evaluation of SBELM against other state- of-the-art techniques. Experimental results show that SBELM achieves fastest execution time with high accuracy over the benchmark face datasets. A MATLAB toolbox of SBELM is also available on our Web site.
KeywordFace recognition Face detection Sparse Bayesian Extreme learning machine
Language英語English
The Source to ArticlePB_Publication
PUB ID15131
Document TypeJournal article
CollectionFaculty of Science and Technology
DEPARTMENT OF ELECTROMECHANICAL ENGINEERING
Corresponding AuthorVong, C. M.
Recommended Citation
GB/T 7714
Vong, C. M.,Tai, K.I.,Pun, C. M.,et al. Fast and accurate face detection by sparse Bayesian extreme learning machine[J]. Neural Computing and Applications (SCI-E), 2015, 1149-1156.
APA Vong, C. M.., Tai, K.I.., Pun, C. M.., & Wong, P. K. (2015). Fast and accurate face detection by sparse Bayesian extreme learning machine. Neural Computing and Applications (SCI-E), 1149-1156.
MLA Vong, C. M.,et al."Fast and accurate face detection by sparse Bayesian extreme learning machine".Neural Computing and Applications (SCI-E) (2015):1149-1156.
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