论文标题
异质脸重新识别的静态排名优化
Mutimodal Ranking Optimization for Heterogeneous Face Re-identification
论文作者
论文摘要
异质的面部重新识别，即横跨不连接的可见光（VIS）和近红外（NIR）摄像机的异质面部的面孔已成为视频监视应用中的一个重要问题。但是，异质的NIR-VIS面之间的大域差异使得面部重新识别的性能显着降解。为了解决这个问题，本文提出了一种多模式融合排名优化算法，用于异质面部重新识别。首先，我们设计了一个异质的面部翻译网络，以获得多模式的面对，包括Nir-Vis/Nir-Nir/Vis-Vis面对，通过NIR-VIS面之间的相互转换。其次，我们提出线性和非线性融合策略，以汇总多模式面对对的初始排名列表，并根据模态互补性获得优化的重新排名列表。实验结果表明，所提出的多模式融合排名优化算法可以有效地利用互补性，并优于SCFACE数据集上的一些相对方法。
Heterogeneous face re-identification, namely matching heterogeneous faces across disjoint visible light (VIS) and near-infrared (NIR) cameras, has become an important problem in video surveillance application. However, the large domain discrepancy between heterogeneous NIR-VIS faces makes the performance of face re-identification degraded dramatically. To solve this problem, a multimodal fusion ranking optimization algorithm for heterogeneous face re-identification is proposed in this paper. Firstly, we design a heterogeneous face translation network to obtain multimodal face pairs, including NIR-VIS/NIR-NIR/VIS-VIS face pairs, through mutual transformation between NIR-VIS faces. Secondly, we propose linear and non-linear fusion strategies to aggregate initial ranking lists of multimodal face pairs and acquire the optimized re-ranked list based on modal complementarity. The experimental results show that the proposed multimodal fusion ranking optimization algorithm can effectively utilize the complementarity and outperforms some relative methods on the SCface dataset.
