Mr. Kun Zhang Profile

Kun Zhang

at Chongqing Normal Univ

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Proceedings Article | 5 October 2021 Paper

CDC-Wasserstein generated adversarial network for locally occluded face image recognition

Kun Zhang, Wenlong Zhang, Shihan Yan, Junrui Jiang, Yuanyuan Li

Proceedings Volume 11911, 1191112 (2021) https://doi.org/10.1117/12.2604573

KEYWORDS: Facial recognition systems, Gallium nitride, Data modeling, Image restoration, Convolution, Image quality, Statistical modeling, Feature extraction, Computer programming, Detection and tracking algorithms

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In the practical application of wisdom education classroom teaching, students' faces may be blocked due to various factors (such as clothing, environment, lighting), resulting in low accuracy and low robustness of face recognition. To solve this problem, we introduce a new image restoration and recognition method is based on WGAN (Wasserstein Generated Adversarial Networks). When using the deep convolution generates adversarial networks for unsupervised training, we add the conditional category label c to guide the generator to generate sample data. At the same time, a double discriminant mechanism is introduced to enhance the feature extraction ability of the model. The local discriminant can better repair the details of the occlusion area, and the global discriminant is responsible for judging the authenticity and overall visual coherence of the restored image. Part of the convolution layer of the global discriminator is used to construct a VGG-like structure network as the feature extractor, which is composed of the full connection layer and the sigmoid layer. It can accelerate the convergence speed of the network and improve the robustness of the method. In order to improve the training stability and reduce overfitting, L2 regularization is added on the basis of context loss to enhance the continuity of local and whole images, and improve the quality of restoration and recognition accuracy. We used the peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and recognition accuracy as evaluation indexes, and achieved good results on CelebA and CelebA-HQ datasets.

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