Face-based gender recognition with small samples generated by DCGAN using CNN
Haoyu Feng · 2023
Gender recognition of face images is an important research field in image classification. Convolutional Neural Network (CNN) has a good performance in this field. To obtain successful results, large and high-quality data sets are essential. However, data scarcity has always been a common challenge in deep learning. The Deep Convolutional Generative Adversarial Network (DCGAN) has a powerful application in data enhancement. The face image generated by DCGAN can be used to enhance face data. In this study, the fake face images generated by DCGAN and the real face images of CelebA datasets are used to train CNN classifier for gender classification of human faces. In the experiment, the training set and test set both are divided into three types, including real face images, fake face images, and real-fake mixed face images. The type of test set is the same as that of the training set, and the number of face images in both the training set and test set is small, including 1000 face images. By comparing the training accuracy and test accuracy of datasets under various conditions in CNN classifiers, it can judge whether the fake face images generated by DCGAN have a good data enhancement effect. The experimental results show that the data enhancement ability of DCGAN is also well applied to CNN classifier, and the face images generated by DCGAN can effectively improve the performance of the CNN classifier to a certain extent.