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.

翻译：人脸属性识别（FAR）在行人重识别、人脸检索和人脸编辑等应用中扮演着关键角色。传统的多任务属性识别方法通常对整个特征图进行特征提取和属性分类，由于依赖全局区域，可能产生冗余特征。为应对这些挑战，我们提出了一种强调选择特定特征区域以实现高效特征学习的新方法。我们引入了掩码引导的多任务网络（MGMTN），该网络集成了自适应掩码学习（AML）与组-全局特征融合（G2FF）以解决上述局限。通过利用预训练的关键点标注模型和全卷积网络，AML能够准确定位关键面部部位（例如眼部和嘴部组），并生成描绘有意义特征区域的组掩码，从而减轻因使用全局区域带来的负迁移。此外，G2FF结合了组特征和全局特征以增强FAR学习，从而实现更精确的属性识别。在两个具有挑战性的人脸属性识别数据集上进行的大量实验证明了MGMTN在提升FAR性能方面的有效性。