Face recognition performance based on deep learning heavily relies on large-scale training data, which is often difficult to acquire in practical applications. To address this challenge, this paper proposes a GAN-based data augmentation method with three key contributions: (1) a residual-embedded generator to alleviate gradient vanishing/exploding problems, (2) an Inception ResNet-V1 based FaceNet discriminator for improved adversarial training, and (3) an end-to-end framework that jointly optimizes data generation and recognition performance. Experimental results demonstrate that our approach achieves stable training dynamics and significantly improves face recognition accuracy by 12.7% on the LFW benchmark compared to baseline methods, while maintaining good generalization capability with limited training samples.
@article{arxiv.2505.11884,
title = {Facial Recognition Leveraging Generative Adversarial Networks},
author = {Zhongwen Li and Zongwei Li and Xiaoqi Li},
journal= {arXiv preprint arXiv:2505.11884},
year = {2026}
}