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http://dx.doi.org/10.15207/JKCS.2018.9.4.047

Facial Local Region Based Deep Convolutional Neural Networks for Automated Face Recognition  

Kim, Kyeong-Tae (Division of Computer and Electronic Systems Eng., Hankuk University of Foreign Studies)
Choi, Jae-Young (Division of Computer and Electronic Systems Eng., Hankuk University of Foreign Studies)
Publication Information
Journal of the Korea Convergence Society / v.9, no.4, 2018 , pp. 47-55 More about this Journal
Abstract
In this paper, we propose a novel face recognition(FR) method that takes advantage of combining weighted deep local features extracted from multiple Deep Convolutional Neural Networks(DCNNs) learned with a set of facial local regions. In the proposed method, the so-called weighed deep local features are generated from multiple DCNNs each trained with a particular face local region and the corresponding weight represents the importance of local region in terms of improving FR performance. Our weighted deep local features are applied to Joint Bayesian metric learning in conjunction with Nearest Neighbor(NN) Classifier for the purpose of FR. Systematic and comparative experiments show that our proposed method is robust to variations in pose, illumination, and expression. Also, experimental results demonstrate that our method is feasible for improving face recognition performance.
Keywords
face recognition; deep convolutional neural network; deep local features; weight combination; facial local region; Joint Bayesian;
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Times Cited By KSCI : 2  (Citation Analysis)
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