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.
翻译:面部分析和识别系统的性能取决于所获取的面部数据的质量,这些数据受到许多因素的影响。因此,自动评估生物鉴别实用性方面的面部数据的质量可有助于检测低质量数据并作出相应决定。这一调查概述了面部图像质量评估文献,主要侧重于可见波长面部图像输入。观察到了一种以深层次学习为基础的方法的趋势,包括最近各种方法之间的明显概念差异,例如将质量评估纳入面部识别模型。除了图像选择外,面部图像质量评估还可以用于其他各种应用设想方案,本文将讨论这些设想方案。指出了一些公开的问题和挑战,即强调可比较性对算法评估的重要性,以及今后工作在创造深层次学习方法方面面临的挑战,这些方法除了提供准确的实用性预测外,还可以解释。