NDLI: Fast and accurate face detection by sparse Bayesian extreme learning machine

Content Provider	Springer Nature Link
Author	Vong, Chi Man Tai, Keng Iam Pun, Chi Man Wong, Pak Kin
Copyright Year	2014
Abstract	Real-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 contribution 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.
Starting Page	1149
Ending Page	1156
Page Count	8
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	26
Issue Number	5
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2014-12-23
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Face recognition Face detection Sparse Bayesian Extreme learning machine Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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