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Cited by 186

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Can facial cosmetics affect the matching accuracy of face recognition systems?

Dantcheva, Antitza, Chen, Cunjian, Ross, Arun·2012·2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)

Abstract

The matching performance of automated face recognition has significantly improved over the past decade. At the same time several challenges remain that significantly affect the deployment of such systems in security applications. In this work, we study the impact of a commonly used face altering technique that has received limited attention in the biometric literature, viz., non-permanent facial makeup. Towards understanding its impact, we first assemble two databases containing face images of subjects, before and after applying makeup. We present experimental results on both databases that reveal the effect of makeup on automated face recognition and suggest that this simple alteration can indeed compromise the accuracy of a biometric system. While these are early results, our findings clearly indicate the need for a better understanding of this face altering scheme and the importance of designing algorithms that can successfully overcome the obstacle imposed by the application of facial makeup.

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Metadata

Authors
Dantcheva, A., Chen, C., & Ross, A.
Venue
2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)
Publisher
IEEE
Published
2012-09
Pages
391-398
OpenAlex
W1976700763
Cited by
186
References
0
FWCI
6.38
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Can facial cosmetics affect the matching accuracy of face recognition systems? https://app.cochat.ai/papers/10.1109/btas.2012.6374605
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Dantcheva, A., Chen, C., & Ross, A. (2012). Can facial cosmetics affect the matching accuracy of face recognition systems? 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS). https://doi.org/10.1109/btas.2012.6374605

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.