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多阶段统计纹理导向GAN用于倾斜面部对面化

Kangli Zeng, Zhongyuan Wang, Tao Lu

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

|March 13, 2025

概括

这项研究介绍了一种新的统计纹理引导生成对抗网络 (STG-GAN),用于高音角人脸识别. 该方法有效地将倾斜的面部进行正面化,在具有挑战性的监控场景中提高识别精度.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
生物识别信息生物识别信息

背景情况:

现有的面部识别方法在监视中常见的高度角度变化中扎.
倾斜面部的自我封闭显著降低了用于准确识别的特征提取.

研究的目的:

通过解决面部正面化的挑战,开发一种强大的高角人脸识别方法.
在极端的姿势变化下提高面部识别系统的准确性和可靠性.

主要方法:

一个统计纹理导向的生成对抗网络 (STG-GAN) 被建议用于倾斜的面部面向.
该STG-GAN使用面部编码器,统计纹理建模和姿势引导解码器.
采用一个分裂与征服的策略,采用多阶段的渐进合成和专业的损失 (内容,身份,对抗性,对比性).

主要成果:

在STG-GAN成功地重塑高角面孔到近似的正面视图.
该方法在多个面部数据集的定性和定量实验中表现出卓越的性能.
实现姿势不变的特征提取和准确的面部部件生成.

结论:

拟议的STG-GAN在姿势不变的面部识别方面取得了重大进展,特别是在高尖角度.
这种方法提高了面部识别在现实世界监控应用中的可靠性.
统计纹理引导的正面化有效地处理自我封闭和立场变化.

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