Abstract
In order to address the impact of large-angle posture changes on face recognition performance, we propose a contour symmetric constraint-generative adversarial network (CSC-GAN) for the multi-pose face recognition. The method employs the convolutional network as the generator for face pose recovery, which introduces the global information of the constrained pose recovery of positive face contour histogram. Meanwhile, the original positive face is used as the discriminator, and the symmetric loss function is added to optimize the learning ability of the network. The positive face with gesture recovery is obtained by striking the balance between training of the generator and discriminator. Then we employed the nearest neighbor classifier to identify. The experimental results show that CSC-GAN obtained good posture reconstruction texture information on the multi-pose face reconstruction. Compared with the traditional deep learning method and 3D method, it also achieves higher recognition rate.
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Acknowledgments
This work is partially supported by the following foundations: the National Natural Science Foundation of China (61661017); the China Postdoctoral Science Fund Project (2016M602923XB); the Natural Science Foundation of Guangxi province (2017GXNSFBA198212, 2016GXNSFAA38014); the Key Laboratory Fund of Cognitive Radio and Information Processing (CRKL160104, CRKL150103, 2011KF11); Innovation Project of GUET Graduate Education (2016YJCXB02); the Scientific and Technological Innovation Ability and Condition Construction Plans of Guangxi (159802521); the Scientific and Technological Bureau of Guilin (20150103-6).
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Ouyang, N., Liu, L., Lin, L. (2020). Multi-pose Face Recognition Based on Contour Symmetric Constraint-Generative Adversarial Network. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_117
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DOI: https://doi.org/10.1007/978-981-13-6504-1_117
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