用生成模型修复遮挡人脸，结合多任务头提升年龄估计精度。
Overcoming Occlusions in the Wild: A Multi-Task Age Head Approach to Age Estimation
- 先用GAN去遮挡，再用Transformer增强特征
- 在三大数据集上平均误差低于3年，最优达2.53年
- 适合实际场景中遮挡严重的年龄识别任务
面部年龄估计在受控条件下已取得显著进展，但在真实世界（即‘野外’）场景中仍具挑战性，尤其当面部部分被遮挡时，可见性降低。为解决此问题，本文提出一种融合生成对抗网络（GAN）与Transformer架构的新方法，实现对遮挡人脸的鲁棒年龄估计。采用SN-Patch GAN有效去除遮挡，同时引入注意力残差卷积模块（ARCM）与Swin Transformer增强特征表示。此外，提出多任务年龄头（MTAH），融合回归与分布学习，进一步提升遮挡下的年龄估计性能。在FG-NET、UTKFace和MORPH数据集上的实验表明，该方法分别达到3.00、4.54和2.53年的平均绝对误差（MAE），优于现有最先进方法。
原文摘要 · Abstract (English)
Facial age estimation has achieved considerable success under controlled conditions. However, in unconstrained real-world scenarios, which are often referred to as 'in the wild', age estimation remains challenging, especially when faces are partially occluded, which may obscure their visibility. To address this limitation, we propose a new approach integrating generative adversarial networks (GANs) and transformer architectures to enable robust age estimation from occluded faces. We employ an SN-Patch GAN to effectively remove occlusions, while an Attentive Residual Convolution Module (ARCM), paired with a Swin Transformer, enhances feature representation. Additionally, we introduce a Multi-Task Age Head (MTAH) that combines regression and distribution learning, further improving age estimation under occlusion. Experimental results on the FG-NET, UTKFace, and MORPH datasets demonstrate that our proposed approach surpasses existing state-of-the-art techniques for occluded facial age estimation by achieving an MAE of $3.00$, $4.54$, and $2.53$ years, respectively.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。