Classification of facial part movement acquired from Kinect V1 and Kinect V2

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

Preview	Pdf Classification of facial part movement acquired from Kinect V1 .pdf Download (144kB) | Preview
	Pdf Classification of facial part movement acquired from Kinect V1_FULL.pdf Restricted to Repository staff only Download (1MB) | Request a copy

Abstract

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.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Classification; Confusion matrix; Face tracking; Facial part movements; Kinect v1; Kinect v2
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division:	Institute of Postgraduate Studies Faculty of Electrical and Electronic Engineering Technology
Depositing User:	Mrs Norsaini Abdul Samat
Date Deposited:	23 Mar 2022 08:19
Last Modified:	23 Mar 2022 08:19
URI:	http://umpir.ump.edu.my/id/eprint/33563
Download Statistic:	View Download Statistics

Actions (login required)

View Item