Sensitivity of Age Estimation Systems to Demographic Factors and Image Quality: Achievements and Challenges
Sensitivity of Age Estimation Systems to Demographic Factors and Image Quality: Achievements and Challenges
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DOI：
10.1109/ijcb48548.2020.9304891
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
A. Akbari;Muhammad Awais;J. Kittler
中科院分区：
文献类型：
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作者：
A. Akbari;Muhammad Awais;J. Kittler
最近，令人印象深刻的不断增长的努力已经致力于具有挑战性的任务，面部年龄估计。新算法所取得的性能改进是在几个具有不同特征的基准测试数据库上测量的，以检查一致性。虽然这本身是一种有价值的方法，但大多数年龄估计相关研究中的一个重要问题是，报告的结果缺乏对内在系统不确定性的评估。因此，需要更深入地研究年龄估计系统在不同情况下的鲁棒性。本文件的目的是对不同的年龄估计系统进行评价和比较分析，以确定趋势及其关键脆弱点。特别是，我们调查了四个年龄估计系统，包括在线微软服务，两个最好的国家的最先进的方法在文献中提倡，以及一种新的年龄估计算法。我们分析了不同的内部和外部因素，包括性别，种族，表情，化妆，照明条件，质量和分辨率的人脸图像，这些年龄估计系统的性能的影响。这种敏感性分析的目标是提供生物识别社区的洞察力和理解的关键主题，相机和环境为基础的因素，影响的整体性能的年龄估计系统的研究。
Recently, impressively growing efforts have been devoted to the challenging task of facial age estimation. The improvements in performance achieved by new algorithms are measured on several benchmarking test databases with different characteristics to check on consistency. While this is a valuable methodology in itself, a significant issue in the most age estimation related studies is that the reported results lack an assessment of intrinsic system uncertainty. Hence, a more in-depth view is required to examine the robustness of age estimation systems in different scenarios. The purpose of this paper is to conduct an evaluative and comparative analysis of different age estimation systems to identify trends, as well as the points of their critical vulnerability. In particular, we investigate four age estimation systems, including the online Microsoft service, two best state-of-the-art approaches advocated in the literature, as well as a novel age estimation algorithm. We analyse the effect of different internal and external factors, including gender, ethnicity, expression, makeup, illumination conditions, quality and resolution of the face images, on the performance of these age estimation systems. The goal of this sensitivity analysis is to provide the biometrics community with the insight and understanding of the critical subject-, camera- and environmental-based factors that affect the overall performance of the age estimation system under study.