融合多种3D人脸重建方法，提升监控场景下跨距离、跨摄像头的人脸验证性能。
Exploring 3D Face Reconstruction and Fusion Methods for Face Verification: A Case-Study in Video Surveillance
- 选用三种主流3D人脸重建算法生成模板，构建多源特征输入
- 在未见距离和相机条件下的测试中，融合系统准确率显著提升
- 适合关注监控场景下鲁棒人脸识别的研究者与工程师
3D人脸重建（3DFR）算法基于特定应用场景的假设，当采集条件（如被摄者与摄像头的距离、摄像头特性）与预期不符时，其性能会受限，这在视频监控中尤为常见。此外，3DFR算法采用不同策略从2D图像重建3D形状，包括统计模型拟合、光度立体法和深度学习。本文选取三种代表当前最先进的3DFR算法，分别作为人脸验证系统的模板集生成器，各系统输出的评分通过评分级融合进行整合。实验表明，在未见过的摄像头距离和相机特性条件下（即跨距离与跨摄像头设置），不同3DFR算法之间的互补性显著提升了验证性能，这为后续探索多3DFR方法集成提供了有力支持。
原文摘要 · Abstract (English)
3D face reconstruction (3DFR) algorithms are based on specific assumptions tailored to distinct application scenarios. These assumptions limit their use when acquisition conditions, such as the subject's distance from the camera or the camera's characteristics, are different than expected, as typically happens in video surveillance. Additionally, 3DFR algorithms follow various strategies to address the reconstruction of a 3D shape from 2D data, such as statistical model fitting, photometric stereo, or deep learning. In the present study, we explore the application of three 3DFR algorithms representative of the SOTA, employing each one as the template set generator for a face verification system. The scores provided by each system are combined by score-level fusion. We show that the complementarity induced by different 3DFR algorithms improves performance when tests are conducted at never-seen-before distances from the camera and camera characteristics (cross-distance and cross-camera settings), thus encouraging further investigations on multiple 3DFR-based approaches.
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