Abstract
3D face reconstruction (3DFR) algorithms are based on specific assumptions tailored to the limits and characteristics of the different application scenarios. In this study, we investigate how multiple state-of-the-art 3DFR algorithms can be used to generate a better representation of subjects, with the final goal of improving the performance of face recognition systems in challenging uncontrolled scenarios. We also explore how different parametric and non-parametric score-level fusion methods can exploit the unique strengths of multiple 3DFR algorithms to enhance biometric recognition robustness. With this goal, we propose a comprehensive analysis of several face recognition systems across diverse conditions, such as varying distances and camera setups, intra-dataset and cross-dataset, to assess the robustness of the proposed ensemble method. The results demonstrate that the distinct information provided by different 3DFR algorithms can alleviate the problem of generalizing over multiple application scenarios. In addition, the present study highlights the potential of advanced fusion strategies to enhance the reliability of 3DFR-based face recognition systems, providing the research community with key insights to exploit them in real-world applications effectively. Although the experiments are carried out in a specific face verification setup, our proposed fusion-based 3DFR methods may be applied to other tasks around face biometrics that are not strictly related to identity recognition.
Abstract (translated)
基于特定假设的3D人脸重建（3DFR）算法适用于不同应用场景的局限性和特点。在这项研究中，我们探讨了如何利用多种最先进的3DFR算法来生成更好的人物表示，以最终提高在具有挑战性的非受控环境下的面部识别系统的性能。此外，我们也探索了不同的参数化和非参数化的评分级融合方法如何利用多个3DFR算法的独特优势来增强生物特征识别的鲁棒性。为了达到这个目标，我们提出了一种对多种人脸识别系统进行全面分析的方法，在各种条件下进行测试，包括不同距离、摄像机设置以及跨数据集等，以评估所提出的集成方法的鲁棒性。研究结果表明，不同的3DFR算法所提供的独特信息可以缓解在多个应用场景中泛化的问题。此外，本研究还强调了高级融合策略增强基于3DFR的人脸识别系统可靠性的潜力，并为研究社区提供了有效利用这些策略的关键见解，以应用于现实世界的应用场景中。尽管实验是在特定的面部验证设置中进行的，但我们的提出的基于融合的3DFR方法可以应用于其他与身份识别不严格相关的面部生物特征任务上。
URL
https://arxiv.org/abs/2504.18886
