论文标题
在闭塞存在下改善面部识别的概括
On Improving the Generalization of Face Recognition in the Presence of Occlusions
论文作者
论文摘要
在本文中，我们介绍了现有2D面部识别方法的关键局限性：对闭塞的鲁棒性。为了完成这项任务，我们系统地分析了面部属性对最先进的面部识别方法性能的影响，并通过广泛的实验对不同类型的闭塞类型下的性能降解进行了定量分析。尽管存在这种阻塞，但我们提出的咬合感知的面部识别方法（OREO）方法学会了判别性面部模板。首先，提出了提取局部身份相关区域的注意机制。然后将本地功能与全局表示形式汇总，以形成一个模板。其次，引入了一种简单但有效的训练策略，以平衡未封闭和遮挡的面部图像。广泛的实验表明，Oreo在基于单像的设置中（10.17％）在遮挡下的概括能力提高了面部识别能力，并且在基于图像集的方案中，在等级-1的准确性方面，基线的表现大约超过基线（2％）。
In this paper, we address a key limitation of existing 2D face recognition methods: robustness to occlusions. To accomplish this task, we systematically analyzed the impact of facial attributes on the performance of a state-of-the-art face recognition method and through extensive experimentation, quantitatively analyzed the performance degradation under different types of occlusion. Our proposed Occlusion-aware face REcOgnition (OREO) approach learned discriminative facial templates despite the presence of such occlusions. First, an attention mechanism was proposed that extracted local identity-related region. The local features were then aggregated with the global representations to form a single template. Second, a simple, yet effective, training strategy was introduced to balance the non-occluded and occluded facial images. Extensive experiments demonstrated that OREO improved the generalization ability of face recognition under occlusions by (10.17%) in a single-image-based setting and outperformed the baseline by approximately (2%) in terms of rank-1 accuracy in an image-set-based scenario.
