论文标题
面部图像质量评估：文献调查
Face Image Quality Assessment: A Literature Survey
论文作者
论文摘要
面部分析和识别系统的性能取决于获得的面部数据的质量，该数据受许多因素的影响。因此，从生物识别效用方面自动评估面部数据的质量对于检测低质量数据并做出相应决策是有用的。这项调查提供了面部图像质量评估文献的概述，该文献主要集中在可见的波长面部图像输入上。观察到基于深度学习的方法的趋势，包括最近的方法之间的显着概念差异，例如将质量评估整合到面部识别模型中。除了选择图像之外，面部图像质量评估也可用于此处讨论的其他各种应用程序场景。 I.A.指出了开放的问题和挑战强调了可比性对算法评估的重要性，以及未来工作的挑战，即创建深度学习方法，这些方法除了提供准确的效用预测外都可以解释。
The performance of face analysis and recognition systems depends on the quality of the acquired face data, which is influenced by numerous factors. Automatically assessing the quality of face data in terms of biometric utility can thus be useful to detect low-quality data and make decisions accordingly. This survey provides an overview of the face image quality assessment literature, which predominantly focuses on visible wavelength face image input. A trend towards deep learning based methods is observed, including notable conceptual differences among the recent approaches, such as the integration of quality assessment into face recognition models. Besides image selection, face image quality assessment can also be used in a variety of other application scenarios, which are discussed herein. Open issues and challenges are pointed out, i.a. highlighting the importance of comparability for algorithm evaluations, and the challenge for future work to create deep learning approaches that are interpretable in addition to providing accurate utility predictions.
