Abstract
The limited resolution of cameras and the wide field of the video surveillance systems lead to low quality captured facial images and difficult to identify. Face super-resolution methods are proposed to enhance the resolution of facial images. However, it remains a challenging issue to restore discriminative features to identify a specific person in surveillance videos. An algorithm that helps face super-resolution and recognition with the aid of discriminative-attributes is proposed in this paper. We introduce discriminative-attributes for face recognition to recover discriminative features in the reconstructed facial images. Attributes with more discriminative power are selected to input the network together with the low-resolution face image. The experimental results of the LFW-a benchmark test show that our method achieves promising results in both subjective visual quality and face recognition accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Dong, C., Loy, C.C., He, K., et al.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 259–307 (2016)
Zhang, K., Zuo, W., Chen, Y., et al.: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142–3155 (2017)
Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., et al.: Generative adversarial nets. In: International Conference on Neural Information Processing Systems, pp. 2672–2680 (2014)
Ledig, C., Theis, L., Huszar, F., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Computer Vision and Pattern Recognition, pp. 105–114 (2017)
Zhang, H., Xu, T., Li, H., et al.: StackGAN: text to photo-realistic image synthesis with stacked generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5908–5916 (2016)
Chen, Y., Tai, Y., Liu, X., et al.: FSRNet: end-to-end learning face super-resolution with facial priors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2492–2501 (2018)
Yu, X., Fernando, B., Hartley, R., et al.: Super-resolving very low-resolution face images with supplementary attributes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 908–917 (2018)
Lee, C.H., Zhang, K., Lee, H.C., et al.: Attribute augmented convolutional neural network for face hallucination. In: IEEE Proceedings of International Conference on Computer Vision and Pattern Recognition workshops, pp. 721–729 (2018)
Liu, W., Wen, Y., Yu, Z., et al.: SphereFace: deep hypersphere embedding for face recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6738–6746 (2017)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Computer Science (2014)
He, K., Zhang, X., Ren, S., et al.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)
Ding, H., Zhou, H., Zhou, S.K., et al.: A deep cascade network for unaligned face attribute classification (2017)
Bing, X., Naiyan, W., Tianqi, C., Mu, L.: Empirical evaluation of rectified activations in convolutional network. arXiv:1505.00853 (2015)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434 (2015)
Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43
Liu, Z., Luo, P., Wang, X., et al.: Deep learning face attributes in the wild. In: IEEE International Conference on Computer Vision, pp. 3730–3738 (2016)
Wolf, L., Hassner, T., Taigman, Y.: Effective unconstrained face recognition by combining multiple descriptors and learned background statistics. IEEE Trans. Pattern Anal. Mach. Intell. 33(10), 1978–1990 (2011)
Zhang, K., Zhang, Z., Li, Z., et al.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016)
Zhang, K., et al.: Super-identity convolutional neural network for face hallucination. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 196–211. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01252-6_12
Acknowledgments
The work in this paper is supported by the National Natural Science Foundation of China (No. 61471013 and No. 61701011), the Beijing Municipal Natural Science Foundation Cooperation Beijing Education Committee (KZ201810005002, KZ201910005007).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Dong, N., Li, X., Li, J., Zhuo, L. (2019). Face Super-Resolution via Discriminative-Attributes. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_41
Download citation
DOI: https://doi.org/10.1007/978-3-030-31723-2_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31722-5
Online ISBN: 978-3-030-31723-2
eBook Packages: Computer ScienceComputer Science (R0)