Facial Recognition Leveraging Generative Adversarial Networks

2025-05-17

AI Summary

To address performance degradation in few-shot face recognition caused by insufficient training data, this paper proposes an end-to-end jointly optimized GAN-based data augmentation framework. Methodologically, we design a residual-embedding generator to mitigate gradient instability; enhance the FaceNet discriminator by integrating Inception and ResNet-v1 architectures to strengthen feature discriminability; and enable joint end-to-end training of the generation and recognition modules. The core contribution lies in unifying generative quality and recognition accuracy into a single optimization objective—thereby avoiding error accumulation inherent in conventional two-stage approaches. Evaluated on the LFW benchmark, our method achieves a 12.7% improvement in recognition accuracy, significantly enhances training stability, and demonstrates superior generalization under few-shot settings.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Natural Language Processing: Generation
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Data and user privacy-enhancing technologies for the Web

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.

Problem

• Limited training data hinders deep learning face recognition performance
• Proposes GAN-based augmentation to generate realistic facial data
• Improves recognition accuracy by 12.7% with stable training

Innovation

• Residual-embedded generator for stable gradients
• Inception ResNet-V1 FaceNet discriminator
• End-to-end joint optimization framework