一种用于监视与侦察中面部属性操纵与重建的深度学习方法
A Deep Learning Approach for Facial Attribute Manipulation and Reconstruction in Surveillance and Reconnaissance
AI总结：
本文提出结合自动编码器、GAN与图像增强模块的数据驱动平台，生成多样化合成面部数据以缓解CelebA训练中的偏见并提升低质监视人脸识别公平性与准确率。
摘要：
监视系统在安全和侦察中发挥着关键作用，但其性能往往因低质量图像和视频而受损，导致人脸识别准确率下降。此外，现有的基于AI的面部分析模型存在与肤色变化和部分遮挡面部相关的偏见，进一步限制了其在多样化真实场景中的有效性。这些挑战源于数据限制和不平衡，即可用训练数据集缺乏足够的多样性，从而造成不公平且不可靠的人脸识别性能。为解决这些问题，我们提出了一个数据驱动平台，通过生成用于弥补数据集偏见的合成训练数据来增强监视能力。我们的方法利用基于深度学习的面部属性操纵与重建，使用自动编码器和生成对抗网络（GANs）创建多样化、高质量的面部数据集。此外，我们的系统集成了图像增强模块，可提高监视画面中低分辨率或被遮挡面部的清晰度。我们使用CelebA数据集评估该方法，结果表明所提出的平台提升了训练数据多样性和模型公平性。本工作有助于减少基于AI的面部分析中的偏见，并提高挑战性环境中的监视准确性，从而带来更公平、更可靠的安全应用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
