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

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

doi:10.1117/12.2604573

5 October 2021 CDC-Wasserstein generated adversarial network for locally occluded face image recognition

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

Proceedings Volume 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning; 1191112 (2021) https://doi.org/10.1117/12.2604573
Event: 2nd International Conference on Computer Vision, Image and Deep Learning, 2021, Liuzhou, China

Abstract

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.

Citation Download Citation

Kun Zhang, Wenlong Zhang, Shihan Yan, Junrui Jiang, and Yuanyuan Li "CDC-Wasserstein generated adversarial network for locally occluded face image recognition", Proc. SPIE 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning, 1191112 (5 October 2021); https://doi.org/10.1117/12.2604573

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KEYWORDS

Facial recognition systems

Gallium nitride

Data modeling

Image restoration

Convolution

Image quality

Statistical modeling

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