使用StyleGAN3反转和改进的微小YOLOv7模型进行面部身份识别
Akhil Kumar1, Swarnava Bhattacharjee2, Ambrish Kumar1
1School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India.
Scientific reports
|March 18, 2025
概括
这项研究引入了FIR-Tiny YOLOv7用于面部身份识别,通过检测操纵的面部属性来提高几次拍摄和传统场景的准确性. 新型号增强了面部识别,尽管外观发生了变化.
科学领域:
- 计算机视觉和人工智能的人工智能
- 生物识别和安全
背景情况:
- 面部识别是复杂的,因为属性操纵,经常在犯罪活动中被利用.
- 现有的方法在少量学习和属性变化方面扎.
研究的目的:
- 为面部属性操纵检测和面部身份识别提出一个单步深度学习解决方案.
- 为了提高识别准确性,在少数拍摄和传统的学习场景.
主要方法:
- 创建了面部属性操纵检测 (FAM) 数据集,其中包含11560张跨越20个身份和38个属性的图像.
- 开发FIR-Tiny YOLOv7模型,将空间变压器块 (STB) 和挤压激发空间金字塔聚合 (SE-SPP) 集成到Tiny YOLOv7.
- 使用StyleGAN3反向生成属性和YOLO格式进行注释.
主要成果:
- FIR-Tiny YOLOv7模型在平均平均精度 (mAP) 中取得了显著的改进:10.0% (一次射击),30.4% (三次射击) 和15.3% (五次射击).
- 与基线Tiny YOLOv7.7相比,在传统的70%-30%分割场景中观察到0.1%的边际改善.
- 在识别面部身份方面表现出卓越的表现,尽管有属性操纵.
结论:
- 拟议的FIR-Tiny YOLOv7模型为强大的面部身份识别提供了一个有希望的方法.
- 在处理不同面部属性操纵和有限数据的场景时有效.
- 面部特征操纵数据集为面部特征操纵检测研究提供了宝贵的资源.
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