publication . Preprint . 2017

Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification

Li, Yi; Song, Lingxiao; Wu, Xiang; He, Ran; Tan, Tieniu;
Open Access English
  • Published: 11 Sep 2017
Abstract
Makeup is widely used to improve facial attractiveness and is well accepted by the public. However, different makeup styles will result in significant facial appearance changes. It remains a challenging problem to match makeup and non-makeup face images. This paper proposes a learning from generation approach for makeup-invariant face verification by introducing a bi-level adversarial network (BLAN). To alleviate the negative effects from makeup, we first generate non-makeup images from makeup ones, and then use the synthesized non-makeup images for further verification. Two adversarial networks in BLAN are integrated in an end-to-end deep network, with the one ...
Subjects
ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITIONComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Computer Science - Computer Vision and Pattern Recognition
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37 references, page 1 of 3

[Alashkar et al. 2017] Alashkar, T.; Jiang, S.; Wang, S.; and Fu, Y. 2017. Examples-rules guided deep neural network for makeup recommendation. In The Thirty-First AAAI Conference on Artificial Intelligence, 941-947. AAAI Press.

[Burlando et al. 2010] Burlando, B.; Verotta, L.; Cornara, L.; and Bottini-Massa, E. 2010. Herbal principles in cosmetics: Properties and mechanisms of action. CRC Press.

[Chen, Dantcheva, and Ross 2016] Chen, C.; Dantcheva, A.; and Ross, A. 2016. An ensemble of patch-based subspaces for makeup-robust face recognition. Information Fusion 32:80-92.

[Dantcheva, Chen, and Ross 2012] Dantcheva, A.; Chen, C.; and Ross, A. 2012. Can facial cosmetics affect the matching accuracy of face recognition systems? In the Fifth International Conference on Biometrics: Theory, Applications and Systems, 391-398. IEEE.

[Goodfellow et al. 2014] Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; and Bengio, Y. 2014. Generative adversarial nets. In Advances in neural information processing systems, 2672- 2680.

[Guo et al. 2016] Guo, Y.; Zhang, L.; Hu, Y.; He, X.; and Gao, J. 2016. Ms-celeb-1m: A dataset and benchmark for large-scale face recognition. In European Conference on Computer Vision, 87-102. Springer.

[Guo, Wen, and Yan 2014] Guo, G.; Wen, L.; and Yan, S.

2014. Face authentication with makeup changes. IEEE Transactions on Circuits and Systems for Video Technology 24(5):814-825.

[He et al. 2017] He, R.; Wu, X.; Sun, Z.; and Tan, T. 2017.

Learning invariant deep representation for nir-vis face recognition. In The Thirty-First AAAI Conference on Artificial Intelligence, 2000-2006. AAAI Press.

[Hinton and Salakhutdinov 2006] Hinton, G. E., and Salakhutdinov, R. R. 2006. Reducing the dimensionality of data with neural networks. science 313(5786):504-507. [OpenAIRE]

[Hu et al. 2013] Hu, J.; Ge, Y.; Lu, J.; and Feng, X. 2013.

Makeup-robust face verification. In International Conference on Acoustics, Speech and Signal Processing, 2342- 2346.

[Huang et al. 2017] Huang, R.; Zhang, S.; Li, T.; and He, R.

2017. Beyond face rotation: Global and local perception gan for photorealistic and identity preserving frontal view synthesis. arXiv preprint arXiv:1704.04086.

37 references, page 1 of 3
Abstract
Makeup is widely used to improve facial attractiveness and is well accepted by the public. However, different makeup styles will result in significant facial appearance changes. It remains a challenging problem to match makeup and non-makeup face images. This paper proposes a learning from generation approach for makeup-invariant face verification by introducing a bi-level adversarial network (BLAN). To alleviate the negative effects from makeup, we first generate non-makeup images from makeup ones, and then use the synthesized non-makeup images for further verification. Two adversarial networks in BLAN are integrated in an end-to-end deep network, with the one ...
Subjects
ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITIONComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Computer Science - Computer Vision and Pattern Recognition
Download from
37 references, page 1 of 3

[Alashkar et al. 2017] Alashkar, T.; Jiang, S.; Wang, S.; and Fu, Y. 2017. Examples-rules guided deep neural network for makeup recommendation. In The Thirty-First AAAI Conference on Artificial Intelligence, 941-947. AAAI Press.

[Burlando et al. 2010] Burlando, B.; Verotta, L.; Cornara, L.; and Bottini-Massa, E. 2010. Herbal principles in cosmetics: Properties and mechanisms of action. CRC Press.

[Chen, Dantcheva, and Ross 2016] Chen, C.; Dantcheva, A.; and Ross, A. 2016. An ensemble of patch-based subspaces for makeup-robust face recognition. Information Fusion 32:80-92.

[Dantcheva, Chen, and Ross 2012] Dantcheva, A.; Chen, C.; and Ross, A. 2012. Can facial cosmetics affect the matching accuracy of face recognition systems? In the Fifth International Conference on Biometrics: Theory, Applications and Systems, 391-398. IEEE.

[Goodfellow et al. 2014] Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; and Bengio, Y. 2014. Generative adversarial nets. In Advances in neural information processing systems, 2672- 2680.

[Guo et al. 2016] Guo, Y.; Zhang, L.; Hu, Y.; He, X.; and Gao, J. 2016. Ms-celeb-1m: A dataset and benchmark for large-scale face recognition. In European Conference on Computer Vision, 87-102. Springer.

[Guo, Wen, and Yan 2014] Guo, G.; Wen, L.; and Yan, S.

2014. Face authentication with makeup changes. IEEE Transactions on Circuits and Systems for Video Technology 24(5):814-825.

[He et al. 2017] He, R.; Wu, X.; Sun, Z.; and Tan, T. 2017.

Learning invariant deep representation for nir-vis face recognition. In The Thirty-First AAAI Conference on Artificial Intelligence, 2000-2006. AAAI Press.

[Hinton and Salakhutdinov 2006] Hinton, G. E., and Salakhutdinov, R. R. 2006. Reducing the dimensionality of data with neural networks. science 313(5786):504-507. [OpenAIRE]

[Hu et al. 2013] Hu, J.; Ge, Y.; Lu, J.; and Feng, X. 2013.

Makeup-robust face verification. In International Conference on Acoustics, Speech and Signal Processing, 2342- 2346.

[Huang et al. 2017] Huang, R.; Zhang, S.; Li, T.; and He, R.

2017. Beyond face rotation: Global and local perception gan for photorealistic and identity preserving frontal view synthesis. arXiv preprint arXiv:1704.04086.

37 references, page 1 of 3
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