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Erschienen in:
2020 | OriginalPaper | Buchkapitel
Multi-pose Face Recognition Based on Contour Symmetric Constraint-Generative Adversarial Network
verfasst von : Ning Ouyang, Liyuan Liu, Leping Lin
Erschienen in: Communications, Signal Processing, and Systems
Verlag: Springer Singapore
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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.