A Deep Learning Approach for Facial Attribute Manipulation and Reconstruction in Surveillance and Reconnaissance

2025-06-06

AI Summary

Face recognition systems exhibit significant performance degradation under low-quality images, partial occlusions, and skin-tone bias, revealing critical shortcomings in generalization and fairness. Method: We propose a data-driven framework that jointly enhances fairness and adapts to image quality, integrating attribute-controllable facial reconstruction with bias-compensated synthetic data generation to construct a highly diverse training set. Our architecture combines a variational autoencoder (VAE) with a conditional generative adversarial network (cGAN), incorporating dedicated super-resolution and occlusion-inpainting modules. Results: Evaluated on CelebA, our method achieves a 23.6% improvement in facial attribute editing accuracy, reduces cross-skin-tone recognition disparity by 41.2%, and boosts recognition accuracy on low-resolution images by 35.8%. These gains demonstrate substantial improvements in model robustness and algorithmic fairness.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

Surveillance systems play a critical role in security and reconnaissance, but their performance is often compromised by low-quality images and videos, leading to reduced accuracy in face recognition. Additionally, existing AI-based facial analysis models suffer from biases related to skin tone variations and partially occluded faces, further limiting their effectiveness in diverse real-world scenarios. These challenges are the results of data limitations and imbalances, where available training datasets lack sufficient diversity, resulting in unfair and unreliable facial recognition performance. To address these issues, we propose a data-driven platform that enhances surveillance capabilities by generating synthetic training data tailored to compensate for dataset biases. Our approach leverages deep learning-based facial attribute manipulation and reconstruction using autoencoders and Generative Adversarial Networks (GANs) to create diverse and high-quality facial datasets. Additionally, our system integrates an image enhancement module, improving the clarity of low-resolution or occluded faces in surveillance footage. We evaluate our approach using the CelebA dataset, demonstrating that the proposed platform enhances both training data diversity and model fairness. This work contributes to reducing bias in AI-based facial analysis and improving surveillance accuracy in challenging environments, leading to fairer and more reliable security applications.

Problem

• Improving low-quality facial images in surveillance systems
• Reducing biases in AI-based facial recognition models
• Enhancing dataset diversity for fairer facial analysis

Innovation

• Deep learning for facial attribute manipulation
• Autoencoders and GANs for synthetic data
• Image enhancement for low-resolution faces