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小脸网:一个基于RetinaFace和AdaFace的小型面部识别方法
Zhengwei Ren1,2, Xinyu Liu1, Jing Xu1,2
1School of Artificial Intelligence, Changchun University of Science and Technology, Changchun 130012, China.
Journal of imaging
|January 24, 2025
概括
这项研究引入了一种改进的面部识别系统,用于监视,提高了使用Retinaface-Resnet和自适应边缘技术的低分辨率和封闭面部的准确性. 新的框架在具有挑战性的现实实验室环境中提高了识别性能.
科学领域:
- 计算机视觉 计算机视觉
- 生物识别信息 生物识别信息
- 人工智能的人工智能
背景情况:
- 传统的面部识别与监控中常见的低分辨率和隐蔽图像作斗争.
- 现有的目标检测算法需要大量的数据,这对于低分辨率面孔来说很少.
- 大学实验室监视面临着独特的挑战,如移动性遮蔽和图像质量差等.
研究的目的:
- 开发一个强大的面部识别框架,用于监控中低分辨率和隐蔽的面部.
- 在具有挑战性的实验室环境中提高面部检测和识别的准确性.
- 解决现有的算法在处理稀缺的低分辨率面部数据集方面的局限性.
主要方法:
- 重建视网膜面部复网,以增强面部检测和定位.
- 整合质量适应边缘 (adaface) 用于低分辨率的面部识别,使用特征规范近似.
- 引入空间深度可分离的卷积,以改善小面和角面的检测.
- 实现一个多对象跟踪算法来处理移动的闭塞.
主要成果:
- 在具有挑战性的WiderFace数据集上实现了96.12%的准确性.
- 在实践实验室监控应用中证明了84.36%的识别准确性.
- 在处理低分辨率,封闭和极端角度面部场景方面的显著改进.
结论:
- 拟议的Retinaface-Resnet和adapface框架有效地解决了低分辨率面部识别的关键挑战.
- 空间卷积和多对象跟踪的整合提高了现实世界监控的稳定性.
- 该框架为在大学实验室等受限制环境中准确识别人员提供了可行的解决方案.
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