Abstract
The face verification system based on deep convolutional neural networks (DCNNs) has achieved great success. The architecture of existing methods is somehow shallow because of the insufficient convolutional layers. And the softmax loss function does not enlarge inter-class variations and minimize the intra-class variations. In this paper, a residual network was adopted as the core architecture to extract the discriminative features and it was trained with joint supervision of center loss and softmax loss. The public available CASIA-Webface dataset was used as the training data to train our model for face verification, and the model was tested on LFW and CAS-PEAL-R1 datasets. Experimental results show that our method achieves higher accuracy on LFW and has better robustness than the shallow model such as VGG Face.
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References
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587. IEEE Press (2014)
Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788. IEEE Press (2016)
He, K., Gkioxari, G., Dollár, P., et al.: Mask R-CNN. arXiv Preprint arXiv:170306870 (2017)
Caelles, S., Maninis, K.K., Pont-Tuset, J., et al.: One-Shot Video Object Segmentation (2016)
Henriques, J.F., Caseiro, R., Martins, P., et al.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015)
Huang, G.B., Ramesh, M., Berg, T., et al.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report 07–49, University of Massachusetts, Amherst (2007)
Taigman, Y., Yang, M., Ranzato, M.A., et al.: Deepface: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708. IEEE Press (2014)
Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1891–1898. IEEE Press (2014)
Sun, Y., Chen, Y., Wang, X., et al.: Deep learning face representation by joint identification-verification. In: Advances in Neural Information Processing Systems, pp. 1988–1996 (2014)
Sun, Y., Wang, X., Tang, X.: Deeply learned face representations are sparse, selective, and robust. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2892–2900. IEEE Press (2015)
Sun, Y., Liang, D., Wang, X., et al.: DeepID3: face recognition with very deep neural networks. arXiv Preprint arXiv:150200873 (2015)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823. IEEE Press (2015)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. arXiv:13112901 (2013)
Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9. IEEE Press (2015)
Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: BMVC, vol. 1, p. 6 (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv Preprint arXiv:14091556 (2014)
Liu, W., Wen, Y., Yu, Z., et al.: SphereFace: deep hypersphere embedding for face recognition. arXiv Preprint arXiv:170408063 (2017)
Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499–515. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_31
Zhang, K., Zhang, Z., Li, Z., et al.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Sig. Process. Lett. 23(10), 1499–1503 (2016)
Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning face representation from scratch. arXiv Preprint arXiv:14117923 (2014)
Deng, J., Dong, W., Socher, R., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 248–255. IEEE Press (2009)
Jia, Y., Shelhamer, E., Donahue, J., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM (2014)
Abadi, M., Barham, P., Chen, J., et al.: TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv Preprint arXiv:160304467 (2016)
Acknowledgments
This work is supported by National Key Research and Development Plan under Grant No. 2016YFC0801005 and the National Nature Science Foundation of China (Grant No. 61503388).
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Zhang, T., Wang, R., Ding, J. (2018). A Discriminative Feature Learning Based on Deep Residual Network for Face Verification. In: Wang, Y., Jiang, Z., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2018. Communications in Computer and Information Science, vol 875. Springer, Singapore. https://doi.org/10.1007/978-981-13-1702-6_41
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