Facial Recognition Leveraging Generative Adversarial Networks

TL;DR

This paper introduces a GAN-based data augmentation framework with novel generator and discriminator designs, significantly enhancing face recognition accuracy especially with limited data.

Contribution

It presents a new end-to-end GAN framework with residual-embedded generator and FaceNet discriminator for improved face recognition performance.

Findings

Achieves 12.7% accuracy improvement on LFW benchmark.

Demonstrates stable training with limited data.

Maintains good generalization capability.

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.