通过局部特征筛选提升人脸属性识别准确率
Mask-Guided Multi-Task Network for Face Attribute Recognition
- 用关键点生成区域掩码，只关注重要面部区域
- 在两个数据集上显著优于传统全局特征方法
- 适合需要精准人脸属性分析的应用场景
人脸属性识别（FAR）在人员重识别、人脸检索和人脸编辑等应用中至关重要。传统多任务属性识别方法通常对整个特征图进行特征提取和分类，依赖全局区域会带来冗余特征。为此，本文提出一种新方法，强调特定特征区域的选择以实现高效特征学习。我们设计了掩码引导的多任务网络（MGMTN），结合自适应掩码学习（AML）与组-全局特征融合（G2FF）来解决上述问题。基于预训练的关键点标注模型和全卷积网络，AML精确定位关键面部区域（如眼睛和嘴部组），生成组掩码以划分有意义的特征区域，从而缓解全局区域带来的负迁移。此外，G2FF融合组内与全局特征，增强FAR学习，实现更精确的属性识别。在两个具有挑战性的面部属性识别数据集上的大量实验表明，MGMTN能有效提升FAR性能。
原文摘要 · Abstract (English)
Face Attribute Recognition (FAR) plays a crucial role in applications such as person re-identification, face retrieval, and face editing. Conventional multi-task attribute recognition methods often process the entire feature map for feature extraction and attribute classification, which can produce redundant features due to reliance on global regions. To address these challenges, we propose a novel approach emphasizing the selection of specific feature regions for efficient feature learning. We introduce the Mask-Guided Multi-Task Network (MGMTN), which integrates Adaptive Mask Learning (AML) and Group-Global Feature Fusion (G2FF) to address the aforementioned limitations. Leveraging a pre-trained keypoint annotation model and a fully convolutional network, AML accurately localizes critical facial parts (e.g., eye and mouth groups) and generates group masks that delineate meaningful feature regions, thereby mitigating negative transfer from global region usage. Furthermore, G2FF combines group and global features to enhance FAR learning, enabling more precise attribute identification. Extensive experiments on two challenging facial attribute recognition datasets demonstrate the effectiveness of MGMTN in improving FAR performance.
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