Abstract
Generative Adversarial Networks (GAN) is an unsupervised algorithm widely used in the field of computer vision. On the basis of traditional generative adversarial networks, a method combining deep Convolutional Neural Networks with Generative Adversarial Network for image processing is proposed. Kernel parameters of the initial convolution of the CNN network are initialized by using the Isotropic Sobel operator in the edge detection operator combined with the convolution kernels in multiple directions. We combined the CenterLoss and SoftmaxLoss as Loss function to improve the supervision. The algorithm can take the intra-class aggregation and class separation into account, making use of MINST and CIFAR-10 data sets to test. Results showed that the model we proposed had obvious advantages in rate of convergence and image recognition rate as compared with other traditional recognition algorithms.
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Acknowledgment
This work has the support of National Key Basic Research Program 973 Project (No. 2013CB329202), 863 Project (No. 2013AA014202), National Natural Science Foundation of China (No. 61571067) and Fund of State Key Laboratory of Information Photonics and Optical Communications (Beijing University of Posts and Telecommunications).
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Chen, H. et al. (2020). Research on Face Recognition Algorithms Based on Deep Convolution Generative Adversarial Networks. In: Wang, Y., Fu, M., Xu, L., Zou, J. (eds) Signal and Information Processing, Networking and Computers. Lecture Notes in Electrical Engineering, vol 628. Springer, Singapore. https://doi.org/10.1007/978-981-15-4163-6_13
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DOI: https://doi.org/10.1007/978-981-15-4163-6_13
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