A Survey on Automatic Face Recognition Using Side-View Face Images
Face recognition from side-view positions poses a considerable challenge in automatic face recognition tasks. Pose variation up to the side-view is an issue of difference in appearance and visibility since only one eye is visible at the side-view poses. Traditionally overlooked, recent advancements...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
|
| Series: | IET Biometrics |
| Online Access: | http://dx.doi.org/10.1049/2024/7886911 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841561496310513664 |
|---|---|
| author | Pinar Santemiz Luuk J. Spreeuwers Raymond N. J. Veldhuis |
| author_facet | Pinar Santemiz Luuk J. Spreeuwers Raymond N. J. Veldhuis |
| author_sort | Pinar Santemiz |
| collection | DOAJ |
| description | Face recognition from side-view positions poses a considerable challenge in automatic face recognition tasks. Pose variation up to the side-view is an issue of difference in appearance and visibility since only one eye is visible at the side-view poses. Traditionally overlooked, recent advancements in deep learning have brought side-view poses to the forefront of research attention. This survey comprehensively investigates methods addressing pose variations up to side-view and categorizes research efforts into feature-based, image-based, and set-based pose handling. Unlike existing surveys addressing pose variations, our emphasis is specifically on extreme poses. We report numerous promising innovations in each category and contemplate the utilization and challenges associated with side-view. Furthermore, we introduce current datasets and benchmarks, conduct performance evaluations across diverse methods, and explore their unique constraints. Notably, while feature-based methods currently stand as the state-of-the-art, our observations suggest that cross-dataset evaluations, attempted by only a few researchers, produce worse results. Consequently, the challenge of matching arbitrary poses in uncontrolled settings persists. |
| format | Article |
| id | doaj-art-80cecbd1243c4f59b262d1c5404370fc |
| institution | Kabale University |
| issn | 2047-4946 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj-art-80cecbd1243c4f59b262d1c5404370fc2025-01-03T01:33:54ZengWileyIET Biometrics2047-49462024-01-01202410.1049/2024/7886911A Survey on Automatic Face Recognition Using Side-View Face ImagesPinar Santemiz0Luuk J. Spreeuwers1Raymond N. J. Veldhuis2Faculty of EEMCSFaculty of EEMCSFaculty of EEMCSFace recognition from side-view positions poses a considerable challenge in automatic face recognition tasks. Pose variation up to the side-view is an issue of difference in appearance and visibility since only one eye is visible at the side-view poses. Traditionally overlooked, recent advancements in deep learning have brought side-view poses to the forefront of research attention. This survey comprehensively investigates methods addressing pose variations up to side-view and categorizes research efforts into feature-based, image-based, and set-based pose handling. Unlike existing surveys addressing pose variations, our emphasis is specifically on extreme poses. We report numerous promising innovations in each category and contemplate the utilization and challenges associated with side-view. Furthermore, we introduce current datasets and benchmarks, conduct performance evaluations across diverse methods, and explore their unique constraints. Notably, while feature-based methods currently stand as the state-of-the-art, our observations suggest that cross-dataset evaluations, attempted by only a few researchers, produce worse results. Consequently, the challenge of matching arbitrary poses in uncontrolled settings persists.http://dx.doi.org/10.1049/2024/7886911 |
| spellingShingle | Pinar Santemiz Luuk J. Spreeuwers Raymond N. J. Veldhuis A Survey on Automatic Face Recognition Using Side-View Face Images IET Biometrics |
| title | A Survey on Automatic Face Recognition Using Side-View Face Images |
| title_full | A Survey on Automatic Face Recognition Using Side-View Face Images |
| title_fullStr | A Survey on Automatic Face Recognition Using Side-View Face Images |
| title_full_unstemmed | A Survey on Automatic Face Recognition Using Side-View Face Images |
| title_short | A Survey on Automatic Face Recognition Using Side-View Face Images |
| title_sort | survey on automatic face recognition using side view face images |
| url | http://dx.doi.org/10.1049/2024/7886911 |
| work_keys_str_mv | AT pinarsantemiz asurveyonautomaticfacerecognitionusingsideviewfaceimages AT luukjspreeuwers asurveyonautomaticfacerecognitionusingsideviewfaceimages AT raymondnjveldhuis asurveyonautomaticfacerecognitionusingsideviewfaceimages AT pinarsantemiz surveyonautomaticfacerecognitionusingsideviewfaceimages AT luukjspreeuwers surveyonautomaticfacerecognitionusingsideviewfaceimages AT raymondnjveldhuis surveyonautomaticfacerecognitionusingsideviewfaceimages |