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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...

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Bibliographic Details
Main Authors: Sheng, Guang Heng, Rosdiyana, Samad, Mahfuzah, Mustafa, Zainah, Md. Zain, Nor Rul Hasma, Abdullah, Dwi, Pebrianti
Format: Conference or Workshop Item
Language:English
English
Published: Springer 2021
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access: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
http://umpir.ump.edu.my/id/eprint/33563/
https://doi.org/10.1007/978-981-15-5281-6_65
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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
http://umpir.ump.edu.my/id/eprint/33563/
https://doi.org/10.1007/978-981-15-5281-6_65

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