Commenced in January 2007
Paper Count: 30127
Adversarial Disentanglement Using Latent Classifier for Pose-Independent Representation
Abstract:The large pose discrepancy is one of the critical challenges in face recognition during video surveillance. Due to the entanglement of pose attributes with identity information, the conventional approaches for pose-independent representation lack in providing quality results in recognizing largely posed faces. In this paper, we propose a practical approach to disentangle the pose attribute from the identity information followed by synthesis of a face using a classifier network in latent space. The proposed approach employs a modified generative adversarial network framework consisting of an encoder-decoder structure embedded with a classifier in manifold space for carrying out factorization on the latent encoding. It can be further generalized to other face and non-face attributes for real-life video frames containing faces with significant attribute variations. Experimental results and comparison with state of the art in the field prove that the learned representation of the proposed approach synthesizes more compelling perceptual images through a combination of adversarial and classification losses.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 39
 X. Chai, S. Shan, X. Chen, and W. Gao, “Locally linear regression for pose-invariant face recognition,” IEEE Transactions on image processing, vol. 16, no. 7, pp. 1716–1725, 2007.
 C. Ding and D. Tao, “A comprehensive survey on pose-invariant face recognition,” ACM Transactions on intelligent systems and technology (TIST), vol. 7, no. 3, p. 37, 2016.
 X. Liu and T. Chen, “Pose-robust face recognition using geometry assisted probabilistic modeling,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1. IEEE, 2005, pp. 502–509.
 X. Liu, J. Rittscher, and T. Chen, “Optimal pose for face recognition,” in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2. IEEE, 2006, pp. 1439–1446.
 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, 2015, pp. 815–823.
 S. Sengupta, J.-C. Chen, C. Castillo, V. M. Patel, R. Chellappa, and D. W. Jacobs, “Frontal to profile face verification in the wild,” in 2016 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2016, pp. 1–9.
 T. Hassner, S. Harel, E. Paz, and R. Enbar, “Effective face frontalization in unconstrained images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 4295–4304.
 M. Kan, S. Shan, H. Chang, and X. Chen, “Stacked progressive auto-encoders (spae) for face recognition across poses,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 1883–1890.
 C. Sagonas, Y. Panagakis, S. Zafeiriou, and M. Pantic, “Robust statistical face frontalization,” in Proceedings of the IEEE international conference on computer vision, 2015, pp. 3871–3879.
 J. Yim, H. Jung, B. Yoo, C. Choi, D. Park, and J. Kim, “Rotating your face using multi-task deep neural network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 676–684.
 X. Zhu, Z. Lei, J. Yan, D. Yi, and S. Z. Li, “High-fidelity pose and expression normalization for face recognition in the wild,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 787–796.
 Z. Zhu, P. Luo, X. Wang, and X. Tang, “Multi-view perceptron: a deep model for learning face identity and view representations,” in Advances in Neural Information Processing Systems, 2014, pp. 217–225.
 O. M. Parkhi, A. Vedaldi, A. Zisserman et al., “Deep face recognition.” in bmvc, vol. 1, no. 3, 2015, p. 6.
 C. Ding and D. Tao, “Robust face recognition via multimodal deep face representation,” IEEE Transactions on Multimedia, vol. 17, no. 11, pp. 2049–2058, 2015.
 I. Masi, S. Rawls, G. Medioni, and P. Natarajan, “Pose-aware face recognition in the wild,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 4838–4846.
 I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems, 2014, pp. 2672–2680.
 S. Li, X. Liu, X. Chai, H. Zhang, S. Lao, and S. Shan, “Morphable displacement field based image matching for face recognition across pose,” in European conference on computer vision. Springer, 2012, pp. 102–115.
 J. Yang, S. E. Reed, M.-H. Yang, and H. Lee, “Weakly-supervised disentangling with recurrent transformations for 3d view synthesis,” in Advances in Neural Information Processing Systems, 2015, pp. 1099–1107.
 C. Ledig, L. Theis, F. Husz´ar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang et al., “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 4681–4690.
 A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” arXiv preprint arXiv:1511.06434, 2015.
 S. Sankaranarayanan, A. Alavi, C. D. Castillo, and R. Chellappa, “Triplet probabilistic embedding for face verification and clustering,” in 2016 IEEE 8th international conference on biometrics theory, applications and systems (BTAS). IEEE, 2016, pp. 1–8.
 Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE transactions on pattern analysis and machine intelligence, vol. 35, no. 8, pp. 1798–1828, 2013.
 F. J. Huang, Y.-L. Boureau, Y. LeCun et al., “Unsupervised learning of invariant feature hierarchies with applications to object recognition,” in 2007 IEEE conference on computer vision and pattern recognition. IEEE, 2007, pp. 1–8.
 L. Tran, X. Yin, and X. Liu, “Disentangled representation learning gan for pose-invariant face recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 1415–1424.
 T. D. Kulkarni, W. F. Whitney, P. Kohli, and J. Tenenbaum, “Deep convolutional inverse graphics network,” in Advances in neural information processing systems, 2015, pp. 2539–2547.
 R. Huang, S. Zhang, T. Li, and R. He, “Beyond face rotation: Global and local perception gan for photorealistic and identity preserving frontal view synthesis,” in Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 2439–2448.
 M. Mirza and S. Osindero, “Conditional generative adversarial nets,” arXiv preprint arXiv:1411.1784, 2014.
 H. Kwak and B.-T. Zhang, “Ways of conditioning generative adversarial networks,” arXiv preprint arXiv:1611.01455, 2016.
 J. T. Springenberg, “Unsupervised and semi-supervised learning with categorical generative adversarial networks,” arXiv preprint arXiv:1511.06390, 2015.
 D. Yi, Z. Lei, S. Liao, and S. Z. Li, “Learning face representation from scratch,” arXiv preprint arXiv:1411.7923, 2014.
 Z. Zhu, P. Luo, X. Wang, and X. Tang, “Deep learning identity-preserving face space,” in Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 113–120.