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231225s2019 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2019.2958404
|2 doi
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Choi, Jae Young
|e verfasserin
|4 aut
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| 245 |
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|a Ensemble of Deep Convolutional Neural Networks with Gabor Face Representations for Face Recognition
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|c 2019
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 27.02.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a Most DCNN-based FR approaches typically employ grayscale or RGB color images as input representations of DCNN architectures. However, other effective face representation methods have been developed and incorporated into current practical FR systems. In light of this fact, the focus of our study is to employ Gabor face representations in the design of DCNN-based FR frameworks to improve FR performance. To this end, we develop a novel "Gabor DCNN (GDCNN)" ensemble method that effectively applies different and multiple Gabor face representations as inputs during the training and testing phases of a DCNN for FR applications. The proposed GDCNN ensemble method primarily consists of two parts: 1) GDCNN ensemble construction and 2) GDCNN ensemble combination. The goal of the former part is to build an ensemble of GDCNN members (i.e., base models), each learned with a particular type of Gabor face representation. The objective of the latter part is to adaptively combine multiple FR outputs of individual GDCNN members. We perform extensive experiments to evaluate our proposed method on four public face databases (DBs) using the associated standard evaluation protocols. Experimental results demonstrate that our approach exhibits significantly better FR performance than typical DCNN-based approaches that rely only on grayscale or color face images as input representations. In addition, the feasibility of our proposed GDCNN ensemble has been successfully demonstrated by making comparisons with other state-of-the-art DCNN-based FR methods
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|a Journal Article
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1 |
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|a Lee, Bum Shik
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g (2019) vom: 18. Dez.
|w (DE-627)NLM09821456X
|x 1941-0042
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|g day:18
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|u http://dx.doi.org/10.1109/TIP.2019.2958404
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