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

TL;DR

This paper introduces a deep learning platform that improves surveillance facial recognition by generating diverse synthetic data and enhancing image quality, addressing biases and low-resolution issues in real-world scenarios.

Contribution

It presents a novel data-driven system using autoencoders and GANs for facial attribute manipulation and reconstruction to reduce bias and improve recognition accuracy.

Findings

Enhanced dataset diversity and fairness demonstrated on CelebA

Improved face recognition accuracy in low-quality surveillance footage

Reduced bias related to skin tone and occlusions

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.