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.
Abstract (translated)
监控系统在安全和侦察中扮演着关键角色，但其性能常常因图像和视频质量低劣而受损，导致面部识别的准确性降低。此外，现有的基于人工智能的面部分析模型受肤色差异及部分遮挡脸部的影响而产生偏差，进一步限制了它们在多样化现实场景中的有效性。这些挑战源于数据限制和不平衡问题，现有训练数据集缺乏足够的多样性，从而导致不公平且不可靠的面部识别性能。 为了解决这些问题，我们提出了一种基于数据驱动平台的方法，通过生成合成训练数据来增强监控能力，并弥补数据集偏差。我们的方法利用深度学习技术进行面部属性操作与重建，使用自动编码器和生成对抗网络（GAN）创建多样化的高质量面部图像数据集。此外，该系统还整合了一个图像增强模块，以提高低分辨率或部分遮挡的面部在监控录像中的清晰度。 我们通过CelebA数据集验证了这种方法的有效性，结果表明所提出的平台可以提升训练数据多样性并改善模型公平性。这项工作有助于减少基于人工智能的面部分析中的偏差，并在充满挑战的环境中提升监控系统的准确性，从而为更公正和可靠的安防应用提供支持。
URL
https://arxiv.org/abs/2506.06578
