Evaluating Scalability in Open-Set Face Recognition Systems

doi:10.1007/978-3-032-03711-4_1

2026-01-01

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DOI：10.1007/978-3-032-03711-4_1

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基于生物特征人脸识别过程的自动化人员识别近年来已越来越多地被用于不同目的和多种应用中。人工智能技术，特别是深度学习，显著提高了在不同场景下正确识别个体的准确性。然而，当人脸识别系统遇到未在训练阶段包含的个体图像时，会产生挑战，可能导致误分类。这对应于开放集人脸识别场景，其中检测未知身份的能力为人脸识别系统的准确性带来了额外挑战。在此背景下，本文分析了随着涉及图像和身份数量的增加，系统可扩展性对识别性能的影响。从预训练模型出发，用系统必须识别的人员图像进行训练，并应用优化未知个体识别的技术，本文介绍了在所进行的测试中观察到的最重要方面。

Keyword:

Face Recognition

Open-Set

Scalability

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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