Abstract
With the continuous impact of epidemics, people have become accustomed to wearing masks. However, most current occluded face recognition (OFR) algorithms lack prior knowledge of occlusions, resulting in poor performance when dealing with occluded faces of varying types and severity in reality. Recognizing occluded faces is still a significant challenge, which greatly affects the convenience of people's daily lives. In this paper, we propose an identity-gated mixture of diffusion experts (MoDE) for OFR. Each diffusion-based generative expert estimates one possible complete image for occluded faces. Considering the random sampling process of the diffusion model, which introduces inevitable differences and variations between the inpainted faces and the real ones. To ensemble effective information from multi-reconstructed faces, we introduce an identity-gating network to evaluate the contribution of each reconstructed face to the identity and adaptively integrate the predictions in the decision space. Moreover, our MoDE is a plug-and-play module for most existing face recognition models. Extensive experiments on three public face datasets and two datasets in the wild validate our advanced performance for various occlusions in comparison with the competing methods.
Abstract (translated)
随着疫情的持续影响，人们已经习惯了佩戴口罩。然而，大多数当前的遮挡面部识别（OFR）算法缺乏对不同类型和程度遮挡情况的先验知识，导致在处理实际生活中各种类型的遮挡面部时表现不佳。因此，识别人脸中的遮挡部分仍然是一个重大挑战，并且严重影响了人们日常生活的便利性。 本文中，我们提出了一种用于OFR的身份门控扩散专家混合模型（MoDE）。每个基于扩散生成的专家都会估算出一张可能完整的未被遮挡的脸部图像。考虑到扩散模型中的随机采样过程会引入修补后的脸部与真实脸部之间的不可避免差异和变化，为了从多个重建的脸部中集成有效的信息，我们引入了一个身份门控网络来评估每个重建脸部对身份识别的重要性，并根据在决策空间中的预测结果进行自适应整合。 此外，我们的MoDE模块可以作为插件方便地应用于大多数现有的面部识别模型。我们在三个公开的人脸数据集和两个野外环境中收集的数据集上进行了广泛的实验，验证了与竞争方法相比，在处理各种遮挡情况时我们模型的先进性能。
URL
https://arxiv.org/abs/2505.04306
