Abstract
The convolutional neural networks (CNN) is one of the most successful deep learning model in the field of face recognition, the different image regions are always treated equally when extracting image features, but in fact different parts of the face play different roles in face recognition. For overcoming this defect, a weighted average pooling algorithm is proposed in this paper, the different weights are assigned to the abstract features from different local image regions in the pooling operation, so as to reflect its different roles in face recognition. The weighted average pooling algorithm is applied to the FaceNet network, and a face recognition algorithm based on the improved FaceNet model is proposed. The simulation experiments show that the proposed face recognition algorithm has higher recognition accuracy than the existing face recognition methods based on deep learning.
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References
Mao, Y.: Research on Face Recognition Algorithm Based on Deep Neural Network. Master, Zhejiang University (2017)
Jing, C., Song, T., Zhuang, L., Liu, G., Wang, L., Liu, K.: A survey of face recognition technology based on deep convolutional neural networks. Comput. Appl. Softw. 35(1), 223–231 (2018). https://doi.org/10.3969/j.issn.1000-386x.2018.01.039
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997). https://doi.org/10.1109/34.598228
Lades, M., Vorbruggen, J.C., Buhmann, J., et al.: Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. Comput. 42(3), 300–311 (1993). https://doi.org/10.1109/12.210173
Qin, H., Yan, J., Li, X., Hu, X.: Joint training of cascaded CNN for face detection. In: 29th IEEE Conference on Computer Vision and Pattern Recognition, pp. 3456–3465. IEEE, Las Vegas (2016). https://doi.org/10.1109/cvpr.2016.376
LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989). https://doi.org/10.1162/neco.1989.1.4.541
Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: DeepFace: Closing the gap to human-level performance in face verification. In: 27th IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708. IEEE, Columbus (2014). https://doi.org/10.1109/CVPR.2014.220
Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: 27th IEEE Conference on Computer Vision and Pattern Recognition, pp. 1891–1898. IEEE, Columbus (2014). https://doi.org/10.1109/cvpr.2014.244
Sun, Y., Chen, Y., Wang, X., Tang, X.: Deep learning face representation by joint identification-verification. In: 28th Annual Conference on Neural Information Processing Systems 2014, pp. 1988–1996. Neural information processing systems foundation, Montreal (2014)
Sun, Y., Wang, X., Tang, X.: Deeply learned face representations are sparse, selective, and robust. In: 28th IEEE Conference on Computer Vision and Pattern Recognition, pp. 2892–2900. IEEE, Boston (2015). https://doi.org/10.1109/cvpr.2015.7298907
Sun, Y., Ding, L., Wang, X., Tang, X.: DeepID3: Face recognition with very deep neural networks. arXiv:1502.00873 (2015)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: 28th IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823. IEEE, Boston (2015). https://doi.org/10.1109/cvpr.2015.7298682
Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016). https://doi.org/10.1109/LSP.2016.2603342
Yi, D., Lei, Z., Liao, S., Li, S. Z.: Learning face representation from scratch. arXiv:1411.7923 (2014)
Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report, University of Massachusetts, Amherst (2007)
Acknowledgements
This work was supported by the Shaanxi Natural Science Foundation (2016JQ5051) and the Department of Education Shaanxi Province (2013JK1023).
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Wei, Q., Mu, T., Han, G., Sun, L. (2019). Face Recognition Based on Improved FaceNet Model. In: Krömer, P., Zhang, H., Liang, Y., Pan, JS. (eds) Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications. ECC 2018. Advances in Intelligent Systems and Computing, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-03766-6_69
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DOI: https://doi.org/10.1007/978-3-030-03766-6_69
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