Abstract
The normalization of faces in the wild is an interesting process which can improve face recognition performances and avoid the complex computation of face generation in cross different variations. In this paper, we propose a multi-objective approach that generates and recognizes normalized faces while preserving their identities. An unsupervised Face Normalization and Recognition framework using discriminant normalized features is presented. This latter is based on an optimized combination of Generative Adversarial Network (GAN) generators and Convolutional Neural Network (CNN) classifiers. The main power of our approach is to generate optimized features representing normalized faces finding a trade-off between improving identity preservation and minimizing the architecture complexity. Additionally, it can be adapted to impaired and unlabeled datasets which can respond to real-world face variations and available data. Experimental results show that the proposed method outperforms other models on face normalization and achieves state-of-the-art frontal-frontal face verification in CFP protocol and face recognition in LFW. The code and results are available at github/FNR-GAN.
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References
Kammoun, A., Slama, R., Tabia, H., Ouni, T., Abid, M.: Generative adversarial networks for face generation: a survey. ACM Comput. Surv. 55(5), 1–37 (2022)
Qian, Y., Deng, W., Hu, J.: Unsupervised face normalization with extreme pose and expression in the wild. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Wang, M., Deng, W.: Deep face recognition: a survey. Neurocomputing 429, 215–244 (2021)
Sengupta, S., Chen, J.-C., Castillo, C., Patel, V.M., Chellappa, R., Jacobs, D.W.: Frontal to profile face verification in the wild. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) (2016)
Huang, G.B., Learned-Miller, E.: Labeled faces in the wild: updates and new reporting procedures. Department Computer Science, University Massachusetts Amherst, Amherst, MA, USA, Technical report, 14-003 (2014)
Learned-Miller, E., Huang, G.B., RoyChowdhury, A., Li, H., Hua, G.: Labeled faces in the wild: a survey. In: Advances in Face Detection and Facial Image Analysis, pp. 189–248 (2016)
Zhang, F., Zhang, T., Mao, Q., Xu, C.: Joint pose and expression modeling for facial expression recognition. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018)
Deng, Y., Yang, J., Chen, D., Wen, F., Tong, X.: Disentangled and controllable face image generation via 3D imitative-contrastive learning. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
Cheng, S., Bronstein, M., Zhou, Y., Kotsia, I., Pantic, M., Zafeiriou, S.: MeshGAN: non-linear 3D morphable models of faces. arXiv (2019)
Gecer, B., Bhattarai, B., Kittler, J., Kim, T.-K.: Semi-supervised adversarial learning to generate photorealistic face images of new identities from 3D morphable model. In: Computer Vision - ECCV 2018, pp. 230–248 (2018)
Gao, Z., Zhang, J., Guo, Y., Ma, C., Zhai, G., Yang, X.: Semi-supervised 3D face representation learning from unconstrained photo collections. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2020)
Tripathy, S., Kannala, J., Rahtu, E.: FACEGAN: facial attribute controllable reenactment GAN. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (2021)
Tripathy, S., Kannala, J., Rahtu, E.: ICface: interpretable and controllable face reenactment using GANs. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) (2020)
Zakharov, E., Shysheya, A., Burkov, E., Lempitsky, V.: Few-shot adversarial learning of realistic neural talking head models. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (2019)
Wiles, O., Koepke, A.S., Zisserman, A.: X2Face: a network for controlling face generation using images, audio, and pose codes. In: Computer Vision - ECCV 2018, pp. 690–706 (2018)
Bansal, A., Ma, S., Ramanan, D., Sheikh, Y.: Recycle-GAN: unsupervised video retargeting. In: Computer Vision - ECCV 2018, pp. 122–138 (2018)
Yin, X., Tai, Y., Huang, Y., Liu, X.: Fan: feature adaptation network for surveillance face recognition and normalization. In: Computer Vision - ACCV 2020, pp. 301–319 (2021)
Hsu, G.-S., Tang, C.-H.: Dual-view normalization for face recognition. IEEE Access 8, 147765–147775 (2020)
Bao, J., Chen, D., Wen, F., Li, H., Hua, G.: Towards open-set identity preserving face synthesis. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018)
Shen, Y., Zhou, B., Luo, P., Tang, X.: FaceFeat-GAN: a two-stage approach for identity-preserving face synthesis. arXiv (2018)
Cheng, J., Chen, Y.-P.P., Li, M., Jiang, Y.-G.: TC-GAN. In: Proceedings of the 27th ACM International Conference on Multimedia (2019)
Yang, H., Huang, D., Wang, Y., Jain, A.K.: Learning face age progression: a pyramid architecture of GANs. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018)
Zhao, J., et al.: Dual-agent GANs for photorealistic and identity preserving profile face synthesis. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Shen, Y., Luo, P., Luo, P., Yan, J., Wang, X., Tang, X.: FaceID-GAN: learning a symmetry three-player GAN for identity-preserving face synthesis. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018)
Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A.: VGGFACE2: a dataset for recognising faces across pose and age. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018) (2018)
Lyons, M.J., Kamachi, M., Gyoba, J.: The Japanese Female Facial Expression (JAFFE) dataset. Semantic scholar (1998)
Shih, F.Y., Chuang, C.F., Wang, P.S.: Performance comparisons of facial expression recognition in JAFFE database. Int. J. Pattern Recognit. Artif. Intell. 22(03), 445–459 (2008)
Kara, Y.E., Genc, G., Aran, O., Akarun, L.: Actively estimating crowd annotation consensus. J. Artif. Intell. Res. 61, 363–405 (2018)
Sankaranarayanan, S., Alavi, A., Castillo, C.D., Chellappa, R.: Triplet probabilistic embedding for face verification and clustering. In: 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS) (2016)
Chen, J.-C., Zheng, J., Patel, V.M., Chellappa, R.: Fisher vector encoded deep convolutional features for unconstrained face verification. In: 2016 IEEE International Conference on Image Processing (ICIP) (2016)
Tran, L., Yin, X., Liu, X.: Disentangled representation learning GAN for pose-invariant face recognition. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
Yin, X., Yu, X., Sohn, K., Liu, X., Chandraker, M.: Towards large-pose face frontalization in the wild. In: 2017 IEEE International Conference on Computer Vision (ICCV) (2017)
Hassner, T., Harel, S., Paz, E., Enbar, R.: Effective face frontalization in unconstrained images. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
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Kammoun, A., Slama, R., Tabia, H., Ouni, T., Abid, M. (2023). FNR-GAN: Face Normalization and Recognition with Generative Adversarial Networks. In: Yan, W.Q., Nguyen, M., Stommel, M. (eds) Image and Vision Computing. IVCNZ 2022. Lecture Notes in Computer Science, vol 13836. Springer, Cham. https://doi.org/10.1007/978-3-031-25825-1_10
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