Optimizing OpenMax Parameters for Open-Set Face Recognition

doi:10.1007/978-3-032-03711-4_20

2026-01-01

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DOI：10.1007/978-3-032-03711-4_20

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人脸识别和分类系统通常被设计用来识别和分类在深度学习系统的训练阶段参与过的人的图像，即已知身份。这对应于闭集场景，通常采用SoftMax层作为卷积神经网络（CNN）的最后一层。然而，该SoftMax层在开集场景中会遇到显著困难，因为它在测试阶段通常无法区分已知和未知身份。因此，OpenMax成为开集场景的解决方案。考虑到这一背景，本文提出了对开集人脸识别场景中OpenMax参数的优化。结果表明，尾大小和待修订的顶级类别数量应分别设置为5和2。相比之下，阈值和距离度量是次要参数，可以取不同值而获得相似结果。此外，通过优化OpenMax参数，先前研究中展示的开集人脸识别场景的准确性得到了提升。

Keyword:

Face Recognition

Open-Set

OpenMax

Deep Learning

期刊

ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2025, PT III

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University of Basque Country

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