CoChat
Sign Up
Cited by 8Augmented Reality ApplicationsAcademic Publishing and Open AccessEthics and Social Impacts of AI

OpenFilter: A Framework to Democratize Research Access to Social Media AR Filters

Open access
Riccio, Piera, Psomas, Bill, Galati, Francesco, Escolano, Francisco, Hofmann, Thomas, Oliver, Nuria·2022·Advances in Neural Information Processing Systems 35
DOI 10.52202/068431-0907
Open this paper in CoChat — free accountOpen PDF

Abstract

Augmented Reality or AR filters on selfies have become very popular on social media platforms for a variety of applications, including marketing, entertainment and aesthetics. Given the wide adoption of AR face filters and the importance of faces in our social structures and relations, there is increased interest by the scientific community to analyze the impact of such filters from a psychological, artistic and sociological perspective. However, there are few quantitative analyses in this area mainly due to a lack of publicly available datasets of facial images with applied AR filters. The proprietary, close nature of most social media platforms does not allow users, scientists and practitioners to access the code and the details of the available AR face filters. Scraping faces from these platforms to collect data is ethically unacceptable and should, therefore, be avoided in research. In this paper, we present OpenFilter, a flexible framework to apply AR filters available in social media platforms on existing large collections of human faces. Moreover, we share FairBeauty and B-LFW, two beautified versions of the publicly available FairFace and LFW datasets and we outline insights derived from the analysis of these beautified datasets.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.52202/068431-0907

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Beauty filters enable a controlled and consistent adjustment of attractiveness at a large scale while keeping the identity of the face constant, which is crucial for isolating the effects of perceived attractiveness from other confounding variables.

    “The manipulation of beauty by means of beauty filters enables a controlled and consistent adjustment of attractiveness at a large scale while keeping the identity of the face constant [45], which is crucial for isolating the effects of perceived attractiveness from other confounding variables, such as facial identity or expression.”

    From What is beautiful is still good: the attractiveness halo effect in the era of beauty filters (2024) · cites this paper as [45]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

No references could be resolved yet.

Research works printed but not linked: 37

  1. [1][1] E. Abi-Jaoude, K. T. Naylor, and A. Pignatiello. Smartphones, social media use and youth mental health. Canadian Medical Association Journal , 192(6):E136-E141, Feb. 2020. -
  2. [2][2] C. Abidin. 'aren't these just young, rich women doing vain things online?': Influencer selfies as subversive frivolity. Social Media + Society , 2(2):2056305116641342, 2016. -
  3. [3][3] G. Appel, L. Grewal, R. Hadi, and A. T. Stephen. The future of social media in marketing. Journal of the Academy of Marketing Science , 48(1):79-95, Oct. 2019. -
  4. [6][6] A. Bharati, M. Vatsa, R. Singh, K. W. Bowyer, and X. Tong. Demography-based facial retouching detection using subclass supervised sparse autoencoder. In 2017 IEEE International Joint Conference on Biometrics (IJCB) , pages 474-482, 2017. -
  5. [7][7] C. Botezatu, M. Ibsen, C. Rathgeb, and C. Busch. Fun selfie filters in face recognition: Impact assessment and removal. arXiv preprint arXiv:2202.06022 , 2022. - · arXiv
  6. [8][8] F. Boutros, N. Damer, F. Kirchbuchner, and A. Kuijper. Elasticface: Elastic margin loss for deep face recognition. arXiv preprint arXiv:2109.09416 , 2021. - · arXiv
  7. [11][11] Q. Bu. The global governance on automated facial recognition (afr): ethical and legal opportunities and privacy challenges. International Cybersecurity Law Review , 2(1):113-145, 2021. -
  8. [12][12] J. Deng, J. Guo, N. Xue, and S. Zafeiriou. Arcface: Additive angular margin loss for deep face recognition. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 4685-4694, 2019. -
  9. [13][13] K. K. Dion. Physical attractiveness and evaluation of children's transgressions. Journal of Personality and Social Psychology , 24(2):207-213, 1972. -
  10. [14][14] A. Dobson. Postfeminist Digital Cultures: Femininity, Social Media, and Self-Representation . Palgrave Macmillan, Australia, 2015. -
  11. [15][15] A. S. Dobson. 'sexy' and 'laddish' girls. Feminist Media Studies , 14(2):253-269, 2014. -
  12. [16][16] A. S. Elias and R. Gill. Beauty surveillance: The digital self-monitoring cultures of neoliberalism. European Journal of Cultural Studies , 21(1):59-77, 2018. -
  13. [17][17] F. M. Felisberti and K. Musholt. Self-face perception: Individual differences and discrepancies associated with mental self-face representation, attractiveness and self-esteem. Psychology & Neuroscience , 7(2):65-72, 2014. -
  14. [18][18] R. Fribourg, E. Peillard, and R. McDonnell. Mirror, mirror on my phone: Investigating dimensions of self-face perception induced by augmented reality filters. In 2021 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) , pages 470-478, 2021. -
  15. [21][21] Y. Guo, L. Zhang, Y. Hu, X. He, and J. Gao. Ms-celeb-1m: A dataset and benchmark for large-scale face recognition. In European conference on computer vision , pages 87-102. Springer, 2016. -
  16. [23][23] P. Hedman, V. Skepetzis, K. Hernandez-Diaz, J. Bigun, and F. Alonso-Fernandez. On the effect of selfie beautification filters on face detection and recognition. arXiv preprint arXiv:2110.08934 , 2021. - · arXiv
  17. [24][24] P. Hedman, V. Skepetzis, K. Hernandez-Diaz, J. Bigun, and F. Alonso-Fernandez. Lfw-beautified: A dataset of face images with beautification and augmented reality filters. arXiv preprint arXiv:2203.06082 , 2022. - · arXiv
  18. [25][25] J. Herrington. Face filters as augmented reality art on social media. In Springer Series on Cultural Computing , pages 297-310. Springer International Publishing, 2022. -
  19. [26][26] A. Hess. The selfie assemblage. International Journal of Communication , 9(1):1629-1646, 2015. -
  20. [27][27] G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller. Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments. In Workshop on Faces in 'Real-Life' Images: Detection, Alignment, and Recognition , Marseille, France, Oct. 2008. Erik Learned-Miller and Andras Ferencz and Frédéric Jurie. -
  21. [28][28] Y. Huang, Y. Wang, Y. Tai, X. Liu, P. Shen, S. Li, J. Li, and F. Huang. Curricularface: Adaptive curriculum learning loss for deep face recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2020. -
  22. [30][30] K. Karkkainen and J. Joo. Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , pages 1548-1558, January 2021. -
  23. [31][31] N. Khunger and H. Pant. Cosmetic Procedures in Adolescents: What's Safe and What Can Wait. Indian Journal of Paediatric Dermatology , 22(1):12-20, 2021. -
  24. [36][36] Q. Meng, S. Zhao, Z. Huang, and F. Zhou. Magface: A universal representation for face recognition and quality assessment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 14225-14234, June 2021. -
  25. [37][37] R. Mihăilă and L. Branişte. Digital semantics of beauty apps and filters: Big data-driven facial retouching, aesthetic self-monitoring devices, and augmented reality-based body-enhancing technologies. Journal of Research in Gender Studies , 11(2):100-112, 2021. -
  26. [39][39] S. J. Nightingale and H. Farid. Ai-synthesized faces are indistinguishable from real faces and more trustworthy. Proceedings of the National Academy of Sciences , 119(8):e2120481119, 2022. -
  27. [40][40] O. Parkhi, A. Vedaldi, and A. Zisserman. Deep face recognition. BMVC 2015 - Proceedings of the British Machine Vision Conference 2015 , pages 1-12, 2019. -
  28. [43][43] I. D. Raji, T. Gebru, M. Mitchell, J. Buolamwini, J. Lee, and E. Denton. Saving face: Investigating the ethical concerns of facial recognition auditing. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , AIES '20, page 145-151, New York, NY, USA, 2020. Association for Computing Machinery. -
  29. [44][44] J. S. Rios, D. J. Ketterer, and D. Y. Wohn. How users choose a face lens on snapchat. In Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing , CSCW '18, page 321-324, New York, NY, USA, 2018. Association for Computing Machinery. -
  30. [47][47] F. Schroff, D. Kalenichenko, and J. Philbin. Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2015. -
  31. [48][48] S. I. Serengil and A. Ozpinar. Lightface: A hybrid deep face recognition framework. In 2020 Innovations in Intelligent Systems and Applications Conference (ASYU) , pages 1-5, 2020. -
  32. [49][49] E. Shein. Filtering for beauty. Communications of the ACM , 64(11):17-19, 2021. -
  33. [51][51] C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi. Inception-v4, inception-resnet and the impact of residual connections on learning. In Thirty-first AAAI conference on artificial intelligence , 2017. -
  34. [52][52] Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. Deepface: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2014. -
  35. [53][53] S. N. Talamas, K. I. Mavor, and D. I. Perrett. Blinded by beauty: Attractiveness bias and accurate perceptions of academic performance. PLOS ONE , 11(2):e0148284, Feb. 2016. -
  36. [54][54] A. Tifentale and L. Manovich. Selfiecity: Exploring Photography and Self-Fashioning in Social Media , pages 109-122. Palgrave Macmillan UK, London, 2015. -
  37. [55][55] K. Tiidenberg. Boundaries and conflict in a nsfw community on tumblr: The meanings and uses of selfies. New Media & Society , 18(8):1563-1578, 2016. -

Cited by

Sorted by citations

Authors
Riccio, P., Psomas, B., Galati, F., Escolano, F., Hofmann, T., & Oliver, N.
ORCID
Venue
Advances in Neural Information Processing Systems 35
Publisher
Neural Information Processing Systems Foundation, Inc. (NeurIPS)
Published
2022
Pages
12491-12503
ISSN
1049-5258 (ISSN-L)
DOI
10.52202/068431-0907
OpenAlex
W7133233703
Cited by
8
References
0
Citations by year
2025: 1 · 2024: 6 · 2023: 1
FWCI
0.73
Type
conference paper
Language
en
Open access
Locations
  • neural information processing systems (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
Generalitat Valenciana
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Augmented Reality Applications
  • Social Sciences › Decision Sciences › Information Systems and Management › Academic Publishing and Open Access
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
augmented reality filters, social media, research accessibility, democratization

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review