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Evaluation of Automatic Facial Wrinkle Detection Algorithms
Facial wrinkles (considered to be natural features) appear as people get older. Wrinkle detection is an important aspect of applications that depend on facial skin changes, such as face age estimation and soft biometrics. While existing wrinkle detection algorithms focus on forehead horizontal lines...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321017/ https://www.ncbi.nlm.nih.gov/pubmed/34460719 http://dx.doi.org/10.3390/jimaging6040017 |
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author | Elbashir, Remah Mutasim Hoon Yap, Moi |
author_facet | Elbashir, Remah Mutasim Hoon Yap, Moi |
author_sort | Elbashir, Remah Mutasim |
collection | PubMed |
description | Facial wrinkles (considered to be natural features) appear as people get older. Wrinkle detection is an important aspect of applications that depend on facial skin changes, such as face age estimation and soft biometrics. While existing wrinkle detection algorithms focus on forehead horizontal lines, it is necessary to develop new methods to detect all wrinkles (vertical and horizontal) on whole face. Therefore, we evaluated the performance of wrinkle detection algorithms on the whole face and proposed an enhancement technique to improve the performance. More specifically, we used 45 images of the Face Recognition Technology dataset (FERET) and 25 images of the Sudanese dataset. For ground truth annotations, the selected images were manually annotated by the researcher. The experiments showed that the method with enhancement performed better at detecting facial wrinkles when compared to the state-of-the-art methods. When evaluated on FERET, the average Jaccard similarity indices were 56.17%, 31.69% and 15.87% for the enhancement method, Hybrid Hessian Filter and Gabor Filter, respectively. |
format | Online Article Text |
id | pubmed-8321017 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83210172021-08-26 Evaluation of Automatic Facial Wrinkle Detection Algorithms Elbashir, Remah Mutasim Hoon Yap, Moi J Imaging Article Facial wrinkles (considered to be natural features) appear as people get older. Wrinkle detection is an important aspect of applications that depend on facial skin changes, such as face age estimation and soft biometrics. While existing wrinkle detection algorithms focus on forehead horizontal lines, it is necessary to develop new methods to detect all wrinkles (vertical and horizontal) on whole face. Therefore, we evaluated the performance of wrinkle detection algorithms on the whole face and proposed an enhancement technique to improve the performance. More specifically, we used 45 images of the Face Recognition Technology dataset (FERET) and 25 images of the Sudanese dataset. For ground truth annotations, the selected images were manually annotated by the researcher. The experiments showed that the method with enhancement performed better at detecting facial wrinkles when compared to the state-of-the-art methods. When evaluated on FERET, the average Jaccard similarity indices were 56.17%, 31.69% and 15.87% for the enhancement method, Hybrid Hessian Filter and Gabor Filter, respectively. MDPI 2020-04-01 /pmc/articles/PMC8321017/ /pubmed/34460719 http://dx.doi.org/10.3390/jimaging6040017 Text en © 2020 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Elbashir, Remah Mutasim Hoon Yap, Moi Evaluation of Automatic Facial Wrinkle Detection Algorithms |
title | Evaluation of Automatic Facial Wrinkle Detection Algorithms |
title_full | Evaluation of Automatic Facial Wrinkle Detection Algorithms |
title_fullStr | Evaluation of Automatic Facial Wrinkle Detection Algorithms |
title_full_unstemmed | Evaluation of Automatic Facial Wrinkle Detection Algorithms |
title_short | Evaluation of Automatic Facial Wrinkle Detection Algorithms |
title_sort | evaluation of automatic facial wrinkle detection algorithms |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321017/ https://www.ncbi.nlm.nih.gov/pubmed/34460719 http://dx.doi.org/10.3390/jimaging6040017 |
work_keys_str_mv | AT elbashirremahmutasim evaluationofautomaticfacialwrinkledetectionalgorithms AT hoonyapmoi evaluationofautomaticfacialwrinkledetectionalgorithms |