Mask-Guided Multi-Task Network for Face Attribute Recognition

• URL: http://arxiv.org/abs/2601.01408v1
• Date: Sun, 04 Jan 2026 07:21:15 GMT
• Title: Mask-Guided Multi-Task Network for Face Attribute Recognition
• Authors: Gong Gao, Zekai Wang, Jian Zhao, Ziqi Xie, Xianhui Liu, Weidong Zhao,
• Abstract summary: Mask-Guided Multi-Task Network (MGMTN) integrates Adaptive Mask Learning (AML) and Group-Global Feature Fusion (G2FF)<n>AML accurately localizes critical facial parts and generates group masks that delineate meaningful feature regions.<n>G2FF combines group and global features to enhance FAR learning, enabling more precise attribute identification.
• Score: 16.994619834678325
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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