ProGAN-based convolutional neural network for improving gender classification

Yutong Chen; Ruixiang Deng; Minghao Zhou

doi:10.1117/12.2668033

28 March 2023 ProGAN-based convolutional neural network for improving gender classification

Yutong Chen, Ruixiang Deng, Minghao Zhou

Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 125663Z (2023) https://doi.org/10.1117/12.2668033
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China

Abstract

To resolve the issue of limited data during the deep learning model training, various data augmentation approaches that increase the amount of data based on existing data are proposed. In this paper, the possibility of using fake face images generated by Progressive Growing GAN (ProGAN) was examined, a type of generative adversarial network, based on the existing limited data set to improve the final accuracy of Convolutional Neural Network (CNN) for image classification. This paper used ProGAN to generate 100 fake faces and added two CNNs to construct two classifiers that determine the gender of the face images. The first gender classifier only used 100 real faces, namely 50 male and 50 female faces, that were randomly selected from CelebFaces Attributes Dataset (CelebA) for training. And for the second gender classifier, this study added 100 fake faces, namely 50 male and 50 female faces, based on the original 100 real faces. It can be observed that the accuracy and fitting degree of the two CNN classifiers through 50 epochs to see the influence of adding fake images. The experimental results showed that adding 100 generated fake images to the original real face images improved the final accuracy of the classifier and reduced the problem of overfitting. Thus, it can be concluded that using images generated by ProGAN is a valid method for improving the performance of the CNN classifier when data is limited.

Citation Download Citation

Yutong Chen, Ruixiang Deng, and Minghao Zhou "ProGAN-based convolutional neural network for improving gender classification", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 125663Z (28 March 2023); https://doi.org/10.1117/12.2668033

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KEYWORDS

Education and training

Data modeling

Gallium nitride

Machine learning

Image processing

Convolutional neural networks

RGB color model

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