Cited By
View all- Xu ZHuang WWang J(2021)A time-varying neural network for solving minimum spanning tree problem on time-varying networkNeurocomputing10.1016/j.neucom.2021.09.040466(139-147)Online publication date: Nov-2021
Deep-learning-based facial classifier applying 3D minimum spanning tree
Pages 59 - 65
Abstract
The study designs a deep-learning-based facial classifier by applying the minimum spanning tree algorithm. A new data format is developed by adding the 3D information into 2D color images. This new type of data can magnificently raise the classifier's accuracy. This data can also recognize the target even in the bad situation. Firstly, the 3D features are extracted from Kinect sensor. After that, some of the extracted features are chosen to compose the minimum spanning tree (MST). The data represented by the minimum spanning tree is then put into the color image to form the new data. The new data will be trained by a deep-learning neural network to create the facial classifier. Comparing with the methods of image-only data with support vector machine (SVM), MST-image data with SVM and image-only data with deep-learning neural network (DLNN), the proposed method of MST-image data with DLNN has the highest identifying accuracy.
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Index Terms
- Deep-learning-based facial classifier applying 3D minimum spanning tree
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Published In
September 2018
253 pages
ISBN:9781450356220
DOI:10.1145/3281375
- Conference Chairs:
- Richard Chbeir,
- Hiroshi Ishikawa,
- Program Chairs:
- Kazutoshi Sumiya,
- Kenji Hatano,
- Mario Koeppen
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Published: 25 September 2018
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MEDES '18
MEDES '18: The 10th International Conference on Management of Digital EcoSystems
September 25 - 28, 2018
Tokyo, Japan
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MEDES '18 Paper Acceptance Rate 29 of 77 submissions, 38%;
Overall Acceptance Rate 267 of 682 submissions, 39%
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View all- Xu ZHuang WWang J(2021)A time-varying neural network for solving minimum spanning tree problem on time-varying networkNeurocomputing10.1016/j.neucom.2021.09.040466(139-147)Online publication date: Nov-2021
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