Face Segmentation: A Journey From Classical to Deep Learning Paradigm, Approaches, Trends, and Directions
<p dir="ltr">Face segmentation represents an active area of research within the bio-metric community in particular and the computer vision community in general. Over the last two decades, methods for face segmentation have received increasing attention due to their diverse applicatio...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , |
| منشور في: |
2020
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513505228488704 |
|---|---|
| author | Khalil Khan (9333883) |
| author2 | Rehan Ullah Khan (9333886) Kashif Ahmad (12592762) Farman Ali (551476) Kyung-Sup Kwak (4831410) |
| author2_role | author author author author |
| author_facet | Khalil Khan (9333883) Rehan Ullah Khan (9333886) Kashif Ahmad (12592762) Farman Ali (551476) Kyung-Sup Kwak (4831410) |
| author_role | author |
| dc.creator.none.fl_str_mv | Khalil Khan (9333883) Rehan Ullah Khan (9333886) Kashif Ahmad (12592762) Farman Ali (551476) Kyung-Sup Kwak (4831410) |
| dc.date.none.fl_str_mv | 2020-03-24T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2020.2982970 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Face_Segmentation_A_Journey_From_Classical_to_Deep_Learning_Paradigm_Approaches_Trends_and_Directions/27037246 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Computer vision and multimedia computation Machine learning Face segmentation face image analysis deep learning machine learning Face Image segmentation Task analysis Semantics Deep learning Computer vision Image analysis |
| dc.title.none.fl_str_mv | Face Segmentation: A Journey From Classical to Deep Learning Paradigm, Approaches, Trends, and Directions |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Face segmentation represents an active area of research within the bio-metric community in particular and the computer vision community in general. Over the last two decades, methods for face segmentation have received increasing attention due to their diverse applications in several human-face image analysis tasks. Although many algorithms have been developed to address the problem, face segmentation is still a challenge not being completely solved, particularly for images taken in wild, unconstrained conditions. In this paper, we present a comprehensive review of face segmentation, focusing on methods for both the constrained and unconstrained environmental conditions. The article illustrates the advantages and disadvantages of previously proposed methods in state-of-the-art (SOA). The approaches presented comprise the seminal works on face segmentation and culminating in SOA approaches of the deep learning architecture. An extensive comparison of the previous approaches is intuitively presented, with a discussion of the potential directions for future research on the topic. We believe this comprehensive review and recap will contribute to a number of application domains, and will augment the knowledge of the research community.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2020.2982970" target="_blank">https://dx.doi.org/10.1109/access.2020.2982970</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_84fb0479362a4b8547bcc625ae5355e2 |
| identifier_str_mv | 10.1109/access.2020.2982970 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/27037246 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Face Segmentation: A Journey From Classical to Deep Learning Paradigm, Approaches, Trends, and DirectionsKhalil Khan (9333883)Rehan Ullah Khan (9333886)Kashif Ahmad (12592762)Farman Ali (551476)Kyung-Sup Kwak (4831410)Information and computing sciencesComputer vision and multimedia computationMachine learningFace segmentationface image analysisdeep learningmachine learningFaceImage segmentationTask analysisSemanticsDeep learningComputer visionImage analysis<p dir="ltr">Face segmentation represents an active area of research within the bio-metric community in particular and the computer vision community in general. Over the last two decades, methods for face segmentation have received increasing attention due to their diverse applications in several human-face image analysis tasks. Although many algorithms have been developed to address the problem, face segmentation is still a challenge not being completely solved, particularly for images taken in wild, unconstrained conditions. In this paper, we present a comprehensive review of face segmentation, focusing on methods for both the constrained and unconstrained environmental conditions. The article illustrates the advantages and disadvantages of previously proposed methods in state-of-the-art (SOA). The approaches presented comprise the seminal works on face segmentation and culminating in SOA approaches of the deep learning architecture. An extensive comparison of the previous approaches is intuitively presented, with a discussion of the potential directions for future research on the topic. We believe this comprehensive review and recap will contribute to a number of application domains, and will augment the knowledge of the research community.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2020.2982970" target="_blank">https://dx.doi.org/10.1109/access.2020.2982970</a></p>2020-03-24T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2020.2982970https://figshare.com/articles/journal_contribution/Face_Segmentation_A_Journey_From_Classical_to_Deep_Learning_Paradigm_Approaches_Trends_and_Directions/27037246CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/270372462020-03-24T12:00:00Z |
| spellingShingle | Face Segmentation: A Journey From Classical to Deep Learning Paradigm, Approaches, Trends, and Directions Khalil Khan (9333883) Information and computing sciences Computer vision and multimedia computation Machine learning Face segmentation face image analysis deep learning machine learning Face Image segmentation Task analysis Semantics Deep learning Computer vision Image analysis |
| status_str | publishedVersion |
| title | Face Segmentation: A Journey From Classical to Deep Learning Paradigm, Approaches, Trends, and Directions |
| title_full | Face Segmentation: A Journey From Classical to Deep Learning Paradigm, Approaches, Trends, and Directions |
| title_fullStr | Face Segmentation: A Journey From Classical to Deep Learning Paradigm, Approaches, Trends, and Directions |
| title_full_unstemmed | Face Segmentation: A Journey From Classical to Deep Learning Paradigm, Approaches, Trends, and Directions |
| title_short | Face Segmentation: A Journey From Classical to Deep Learning Paradigm, Approaches, Trends, and Directions |
| title_sort | Face Segmentation: A Journey From Classical to Deep Learning Paradigm, Approaches, Trends, and Directions |
| topic | Information and computing sciences Computer vision and multimedia computation Machine learning Face segmentation face image analysis deep learning machine learning Face Image segmentation Task analysis Semantics Deep learning Computer vision Image analysis |