Classification of transient facial wrinkle

Classification of transient wrinkle is an important application in research related to the skin aging, facial expression and skin analysis. Many researches have been done in the detection or classification of wrinkle, but it still needs some improvement in the algorithms, either in feature extractio...

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Main Authors:	Rosdiyana, Samad, Mohammad Zarif, Rosli, Nor Rul Hasma, Abdullah, Mahfuzah, Mustafa, Dwi, Pebrianti, Nurul Hazlina, Noordin
Format:	Conference or Workshop Item
Language:	English English
Published:	Springer Singapore 2019
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/25026/ http://umpir.ump.edu.my/id/eprint/25026/ http://umpir.ump.edu.my/id/eprint/25026/ http://umpir.ump.edu.my/id/eprint/25026/1/51.%20Classification%20of%20transient%20facial%20wrinkle.pdf http://umpir.ump.edu.my/id/eprint/25026/2/51.1%20Classification%20of%20transient%20facial%20wrinkle.pdf

id	ump-25026
recordtype	eprints
spelling	ump-250262019-11-12T04:18:58Z http://umpir.ump.edu.my/id/eprint/25026/ Classification of transient facial wrinkle Rosdiyana, Samad Mohammad Zarif, Rosli Nor Rul Hasma, Abdullah Mahfuzah, Mustafa Dwi, Pebrianti Nurul Hazlina, Noordin TK Electrical engineering. Electronics Nuclear engineering Classification of transient wrinkle is an important application in research related to the skin aging, facial expression and skin analysis. Many researches have been done in the detection or classification of wrinkle, but it still needs some improvement in the algorithms, either in feature extraction part or classification. In this study, classification of transient wrinkle is proposed by using wrinkle features that extracted from the combination algorithms of Gabor wavelet and Canny operator. The facial wrinkle features are then classified by using artificial intelligent method which are Artificial Neural Network (ANN) and K-Nearest Neighbors (KNN). These two classifiers are trained and tested, and then the performance of each classifier is compared to getting the higher accuracy. 130 face images from various sources are used in the experiments, 65 of the total face images contains wrinkles on the forehead. The results show that ANN classifier only achieves 96.67% accuracy, while the KNN classifier obtained the highest accuracy with 100%. The comparison demonstrates that KNN works well in this classification. This result also proved that the extraction of facial wrinkle using a combination of Gabor and Canny detector is successful. Springer Singapore 2019 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25026/1/51.%20Classification%20of%20transient%20facial%20wrinkle.pdf pdf en http://umpir.ump.edu.my/id/eprint/25026/2/51.1%20Classification%20of%20transient%20facial%20wrinkle.pdf Rosdiyana, Samad and Mohammad Zarif, Rosli and Nor Rul Hasma, Abdullah and Mahfuzah, Mustafa and Dwi, Pebrianti and Nurul Hazlina, Noordin (2019) Classification of transient facial wrinkle. In: Proceedings of the 10th National Technical Seminar on Underwater System Technology 2018, 27-28 September 2018 , Universiti Malaysia Pahang. pp. 391-403., 538. ISBN 978-981-13-3708-6 https://doi.org/10.1007/978-981-13-3708-6_34 DOI: https://doi.org/10.1007/978-981-13-3708-6_34
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Rosdiyana, Samad Mohammad Zarif, Rosli Nor Rul Hasma, Abdullah Mahfuzah, Mustafa Dwi, Pebrianti Nurul Hazlina, Noordin Classification of transient facial wrinkle
description	Classification of transient wrinkle is an important application in research related to the skin aging, facial expression and skin analysis. Many researches have been done in the detection or classification of wrinkle, but it still needs some improvement in the algorithms, either in feature extraction part or classification. In this study, classification of transient wrinkle is proposed by using wrinkle features that extracted from the combination algorithms of Gabor wavelet and Canny operator. The facial wrinkle features are then classified by using artificial intelligent method which are Artificial Neural Network (ANN) and K-Nearest Neighbors (KNN). These two classifiers are trained and tested, and then the performance of each classifier is compared to getting the higher accuracy. 130 face images from various sources are used in the experiments, 65 of the total face images contains wrinkles on the forehead. The results show that ANN classifier only achieves 96.67% accuracy, while the KNN classifier obtained the highest accuracy with 100%. The comparison demonstrates that KNN works well in this classification. This result also proved that the extraction of facial wrinkle using a combination of Gabor and Canny detector is successful.
format	Conference or Workshop Item
author	Rosdiyana, Samad Mohammad Zarif, Rosli Nor Rul Hasma, Abdullah Mahfuzah, Mustafa Dwi, Pebrianti Nurul Hazlina, Noordin
author_facet	Rosdiyana, Samad Mohammad Zarif, Rosli Nor Rul Hasma, Abdullah Mahfuzah, Mustafa Dwi, Pebrianti Nurul Hazlina, Noordin
author_sort	Rosdiyana, Samad
title	Classification of transient facial wrinkle
title_short	Classification of transient facial wrinkle
title_full	Classification of transient facial wrinkle
title_fullStr	Classification of transient facial wrinkle
title_full_unstemmed	Classification of transient facial wrinkle
title_sort	classification of transient facial wrinkle
publisher	Springer Singapore
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/25026/ http://umpir.ump.edu.my/id/eprint/25026/ http://umpir.ump.edu.my/id/eprint/25026/ http://umpir.ump.edu.my/id/eprint/25026/1/51.%20Classification%20of%20transient%20facial%20wrinkle.pdf http://umpir.ump.edu.my/id/eprint/25026/2/51.1%20Classification%20of%20transient%20facial%20wrinkle.pdf
first_indexed	2023-09-18T22:38:13Z
last_indexed	2023-09-18T22:38:13Z
_version_	1777416738602745856

Classification of transient facial wrinkle

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