Classification of facial part movement acquired from Kinect V1 and Kinect V2
The aim of this study is to determine the motion sensor with better performance in facial part movements recognition among Kinect v1 and Kinect v2. This study has applied some classification methods such as neural network, complex decision tree, cubic SVM, fine Gaussian SVM, fine kNN and QDA in the...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Springer
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/33563/ http://umpir.ump.edu.my/id/eprint/33563/1/Classification%20of%20facial%20part%20movement%20acquired%20from%20Kinect%20V1%20.pdf http://umpir.ump.edu.my/id/eprint/33563/2/Classification%20of%20facial%20part%20movement%20acquired%20from%20Kinect%20V1_FULL.pdf |
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| author | Sheng, Guang Heng Rosdiyana, Samad Mahfuzah, Mustafa Zainah, Md. Zain Nor Rul Hasma, Abdullah Dwi, Pebrianti |
| author_facet | Sheng, Guang Heng Rosdiyana, Samad Mahfuzah, Mustafa Zainah, Md. Zain Nor Rul Hasma, Abdullah Dwi, Pebrianti |
| author_sort | Sheng, Guang Heng |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The aim of this study is to determine the motion sensor with better performance in facial part movements recognition among Kinect v1 and Kinect v2. This study has applied some classification methods such as neural network, complex decision tree, cubic SVM, fine Gaussian SVM, fine kNN and QDA in the dataset obtained from Kinect v1 and Kinect v2. The facial part movement is detected and extracted in 11 features and 15 classes. The chosen classifications are then applied to train and test the dataset. Kinect sensor that has the dataset with highest testing accuracy will be selected to develop an assistive facial exercise application in terms of tracking performance and detection accuracy. |
| first_indexed | 2025-11-15T03:10:36Z |
| format | Conference or Workshop Item |
| id | ump-33563 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:10:36Z |
| publishDate | 2021 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-335632022-03-23T08:19:00Z http://umpir.ump.edu.my/id/eprint/33563/ Classification of facial part movement acquired from Kinect V1 and Kinect V2 Sheng, Guang Heng Rosdiyana, Samad Mahfuzah, Mustafa Zainah, Md. Zain Nor Rul Hasma, Abdullah Dwi, Pebrianti TK Electrical engineering. Electronics Nuclear engineering The aim of this study is to determine the motion sensor with better performance in facial part movements recognition among Kinect v1 and Kinect v2. This study has applied some classification methods such as neural network, complex decision tree, cubic SVM, fine Gaussian SVM, fine kNN and QDA in the dataset obtained from Kinect v1 and Kinect v2. The facial part movement is detected and extracted in 11 features and 15 classes. The chosen classifications are then applied to train and test the dataset. Kinect sensor that has the dataset with highest testing accuracy will be selected to develop an assistive facial exercise application in terms of tracking performance and detection accuracy. Springer 2021 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/33563/1/Classification%20of%20facial%20part%20movement%20acquired%20from%20Kinect%20V1%20.pdf pdf en http://umpir.ump.edu.my/id/eprint/33563/2/Classification%20of%20facial%20part%20movement%20acquired%20from%20Kinect%20V1_FULL.pdf Sheng, Guang Heng and Rosdiyana, Samad and Mahfuzah, Mustafa and Zainah, Md. Zain and Nor Rul Hasma, Abdullah and Dwi, Pebrianti (2021) Classification of facial part movement acquired from Kinect V1 and Kinect V2. In: Lecture Notes in Electrical Engineering; 11th National Technical Symposium on Unmanned System Technology, NUSYS 2019 , 2 - 3 December 2019 , Kuantan, Malaysia. 911 -924., 666. ISSN 1876-1100 ISBN 9789811552816 (Published) https://doi.org/10.1007/978-981-15-5281-6_65 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Sheng, Guang Heng Rosdiyana, Samad Mahfuzah, Mustafa Zainah, Md. Zain Nor Rul Hasma, Abdullah Dwi, Pebrianti Classification of facial part movement acquired from Kinect V1 and Kinect V2 |
| title | Classification of facial part movement acquired from Kinect V1 and Kinect V2 |
| title_full | Classification of facial part movement acquired from Kinect V1 and Kinect V2 |
| title_fullStr | Classification of facial part movement acquired from Kinect V1 and Kinect V2 |
| title_full_unstemmed | Classification of facial part movement acquired from Kinect V1 and Kinect V2 |
| title_short | Classification of facial part movement acquired from Kinect V1 and Kinect V2 |
| title_sort | classification of facial part movement acquired from kinect v1 and kinect v2 |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/33563/ http://umpir.ump.edu.my/id/eprint/33563/ http://umpir.ump.edu.my/id/eprint/33563/1/Classification%20of%20facial%20part%20movement%20acquired%20from%20Kinect%20V1%20.pdf http://umpir.ump.edu.my/id/eprint/33563/2/Classification%20of%20facial%20part%20movement%20acquired%20from%20Kinect%20V1_FULL.pdf |