空间相关性指导的交叉尺度特征融合用于年龄和性别估计
Shiyi Jiang1, Qing Ji1, Hukui Shi1
1Guizhou Mobile Information Technology Co., Ltd, Guiyang, 550002, China.
Scientific reports
|July 2, 2025
概括
这项研究引入了SCGNet,用于准确的年龄和性别识别,即使是严重的面部遮. 这种新型网络有效地利用身体特征,在具有挑战性的,不受控制的场景中取得最先进的结果.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 生物识别信息 生物识别信息
背景情况:
- 面部识别系统与封闭和不受控制的环境作斗争.
- 当面部特征缺失或模糊时,现有的方法会失败.
研究的目的:
- 开发一个强大的年龄和性别识别网络,尽管面部严重封闭.
- 克服面部特征依赖的识别系统的局限性.
主要方法:
- 拟议的空间相关性引导跨尺度特征融合网络 (SCGNet).
- 集成的跨度组合 (CSC) 和局部特征引导融合 (LFGF) 模块.
- 介绍了使用Getis-Ord Gi*统计数据进行空间相关组合分析 (SCCA),用于特征重组和热点检测.
主要成果:
- 在多个数据集上实现了最先进的年龄估计 (MAE 4.01%) 和性别分类准确性.
- 保持高的性别准确性 (97.32%) 即使完全封闭了脸部.
- 在年龄预测方面表现出卓越的跨场景适应性 (73.30% CS@5).
结论:
- SCGNet有效地利用非面部特征进行强大的身份分析.
- 空间相关性建模在封闭的场景中提高了识别精度.
- 该方法为人机交互和智能安全提供了显著的改进.
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