基于锚点损失优化的细粒度人脸检测方法

doi:10.16451/j.cnki.issn1003-6059.202505006

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Abstract In unconstrained environments, face images exhibit the characteristics of complex backgrounds and varying scales. Current face detectors suffer from an imbalanced number of anchors matched to the faces in label assignment and the receptive field growth limited by convolutional kernels in feature extraction. These issues lead to the difficulty of fine-grained optimization of the network. To address these issues, a fine-grained face detection method based on anchor loss optimization(FALO) is proposed. First, the relationship between the number of anchors matched to the faces and the loss is analyzed, and an anchor loss optimization algorithm is introduced to fine-tune the classification and localization loss during training. Second, a context feature fusion module is designed to effectively extract multi-scale features from the background. Finally, convolutional neural networks and self-attention mechanisms are considered comprehensively, and a self-attention auxiliary branch is constructed to supplement the receptive field of the detector and improve the attention to faces with different aspect ratios. Experiments on multiple datasets demonstrate that FALO achieves both real-time computational efficiency and high-precision detection, and it exhibits certain advantages in hard sample mining.

Key words： Key Words Face Detection Hard Sample Mining Feature Fusion Fine Granularity Self-Attention Mechanism

Received: 10 April 2025

ZTFLH:

TP391

Fund:National Natural Science Foundation of China(No.62372214), Suzhou Science and Technology Project(No.SGC2021070)

Corresponding Authors: LI Guanghui, Ph.D., professor. His research interests include internet of things, edge computing, non-destructive testing, and integrated circuit design verification.

About author:: LIU Jialong, Master student. His research interests include face detection and model compression.
DAI Chenglong, Ph.D., associate profe?ssor. His research interests include brain?computer interface, EEG signal processing, and data mining.

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Cite this article:

LIU Jialong,LI Guanghui,DAI Chenglong. Fine-Grained Face Detection Method Based on Anchor Loss Optimization[J]. Pattern Recognition and Artificial Intelligence, 2025, 38(5): 457-471.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202505006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2025/V38/I5/457

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