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.

翻译：基于深度学习的面部识别性能严重依赖于大规模训练数据，而这在实际应用中往往难以获取。为应对这一挑战，本文提出了一种基于GAN的数据增强方法，其包含三项关键贡献：(1) 嵌入残差结构的生成器以缓解梯度消失/爆炸问题，(2) 基于Inception ResNet-V1的FaceNet判别器以改进对抗训练效果，(3) 可联合优化数据生成与识别性能的端到端框架。实验结果表明，该方法实现了稳定的训练动态，在LFW基准测试中相比基线方法将面部识别准确率显著提升了12.7%，同时在有限训练样本下保持了良好的泛化能力。