Facial Recognition Leveraging Generative Adversarial Networks

Zhongwen Li; Zongwei Li; Xiaoqi Li

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arXiv · 2505.11884

Facial Recognition Leveraging Generative Adversarial Networks

Zhongwen Li·Zongwei Li·Xiaoqi Li

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Abstract

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.

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BibTeX RIS

Zhongwen Li, Zongwei Li, Xiaoqi Li. 2025-05-17. Facial Recognition Leveraging Generative Adversarial Networks. https://arxiv.org/abs/2505.11884

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