# Facial Recognition Leveraging Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2505.11884 · HTML: https://mlsanity.com/paper/2505.11884/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 53 references that are in the map.
- First posted on arXiv 17 May 2025.

## Builds on (most cited first, 16 of 16)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
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- [Smart Contracts in the Real World: A Statistical Exploration of External Data Dependencies](https://mlsanity.com/paper/2406.13253/) (2024)
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- [SmartBugBert: BERT-Enhanced Vulnerability Detection for Smart Contract Bytecode](https://mlsanity.com/paper/2504.05002/) (2025)
- [Enhancing Smart Contract Vulnerability Detection in DApps Leveraging Fine-Tuned LLM](https://mlsanity.com/paper/2504.05006/) (2025)
- [Survey on Quality Assurance of Smart Contracts](https://mlsanity.com/paper/2311.00270/) (2023)
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- [Demystifying Invariant Effectiveness for Securing Smart Contracts](https://mlsanity.com/paper/2404.14580/) (2024)
- [Characterizing the Solana NFT Ecosystem](https://mlsanity.com/paper/2403.10879/) (2024)

## Abstract (arXiv)

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.