合成面部数据生成技术在人脸识别中的比较研究
A Comparative Study on Synthetic Facial Data Generation Techniques for Face Recognition
AI总结：
本研究比较了不同合成面部数据生成技术在人脸识别中的有效性，评估了多个数据集上的性能，指出合成数据在捕捉现实变化方面的潜力及进一步研究的必要性。
摘要：
人脸识别已成为用于身份验证和识别的广泛使用方法，应用于安全访问和寻找失踪人员。其成功很大程度上归功于深度学习，它利用大规模数据集和有效的损失函数来学习判别特征。尽管取得了这些进展，人脸识别仍然面临可解释性、种族偏见、隐私和对老化、姿态变化、光照变化、遮挡和面部表情的鲁棒性等挑战。隐私法规也导致了一些数据集的退化，引发了法律、伦理和隐私方面的关切。合成面部数据生成已被提出作为有前途的解决方案。它缓解了隐私问题，使能够进行受控面部属性的实验，减轻种族偏见，并提供补充数据以提高在真实数据上训练的模型。本研究比较了使用不同技术生成的合成面部数据集在人脸识别任务中的有效性。我们评估了在八个领先数据集上的准确性、排名1、排名5以及在假阳性率为0.01%时的真正阳性率，提供了未广泛探讨的比较分析。结果表明，合成数据能够捕捉到现实中的变化，同时强调了需要进一步研究以缩小与真实数据的性能差距。扩散模型、生成对抗网络和3D模型等技术显示出显著进展；然而，仍然存在挑战。
英文摘要：
Facial recognition has become a widely used method for authentication and identification, with applications for secure access and locating missing persons. Its success is largely attributed to deep learning, which leverages large datasets and effective loss functions to learn discriminative features. Despite these advances, facial recognition still faces challenges in explainability, demographic bias, privacy, and robustness to aging, pose variations, lighting changes, occlusions, and facial expressions. Privacy regulations have also led to the degradation of several datasets, raising legal, ethical, and privacy concerns. Synthetic facial data generation has been proposed as a promising solution. It mitigates privacy issues, enables experimentation with controlled facial attributes, alleviates demographic bias, and provides supplementary data to improve models trained on real data. This study compares the effectiveness of synthetic facial datasets generated using different techniques in facial recognition tasks. We evaluate accuracy, rank-1, rank-5, and the true positive rate at a false positive rate of 0.01% on eight leading datasets, offering a comparative analysis not extensively explored in the literature. Results demonstrate the ability of synthetic data to capture realistic variations while emphasizing the need for further research to close the performance gap with real data. Techniques such as diffusion models, GANs, and 3D models show substantial progress; however, challenges remain.
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。
