Facial Recognition Leveraging Generative Adversarial Networks

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{li2025_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={ 2025 }
}