A Comprehensive Evaluation Framework for the Study of the Effects of Facial Filters on Face Recognition Accuracy
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911073210204160 |
|---|---|
| author | Ozturk, Kagan Conwill, Louisa Gutierrez, Jacob Bowyer, Kevin Scheirer, Walter J. |
| author_facet | Ozturk, Kagan Conwill, Louisa Gutierrez, Jacob Bowyer, Kevin Scheirer, Walter J. |
| contents | Facial filters are now commonplace for social media users around the world. Previous work has demonstrated that facial filters can negatively impact automated face recognition performance. However, these studies focus on small numbers of hand-picked filters in particular styles. In order to more effectively incorporate the wide ranges of filters present on various social media applications, we introduce a framework that allows for larger-scale study of the impact of facial filters on automated recognition. This framework includes a controlled dataset of face images, a principled filter selection process that selects a representative range of filters for experimentation, and a set of experiments to evaluate the filters' impact on recognition. We demonstrate our framework with a case study of filters from the American applications Instagram and Snapchat and the Chinese applications Meitu and Pitu to uncover cross-cultural differences. Finally, we show how the filtering effect in a face embedding space can easily be detected and restored to improve face recognition performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_17729 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Evaluation Framework for the Study of the Effects of Facial Filters on Face Recognition Accuracy Ozturk, Kagan Conwill, Louisa Gutierrez, Jacob Bowyer, Kevin Scheirer, Walter J. Computer Vision and Pattern Recognition Facial filters are now commonplace for social media users around the world. Previous work has demonstrated that facial filters can negatively impact automated face recognition performance. However, these studies focus on small numbers of hand-picked filters in particular styles. In order to more effectively incorporate the wide ranges of filters present on various social media applications, we introduce a framework that allows for larger-scale study of the impact of facial filters on automated recognition. This framework includes a controlled dataset of face images, a principled filter selection process that selects a representative range of filters for experimentation, and a set of experiments to evaluate the filters' impact on recognition. We demonstrate our framework with a case study of filters from the American applications Instagram and Snapchat and the Chinese applications Meitu and Pitu to uncover cross-cultural differences. Finally, we show how the filtering effect in a face embedding space can easily be detected and restored to improve face recognition performance. |
| title | A Comprehensive Evaluation Framework for the Study of the Effects of Facial Filters on Face Recognition Accuracy |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.17729 |