  Quality scores provide a measure to evaluate the utility of biometric samples
for biometric recognition. Biometric recognition systems require high-quality
samples to achieve optimal performance. This paper focuses on face images and
the measurement of face image utility with general and face-specific image
quality metrics. While face-specific metrics rely on features of aligned face
images, general image quality metrics can be used on the global image and
relate to human perceptions. In this paper, we analyze the gap between the
general image quality metrics and the face image quality metrics. Our
contribution lies in a thorough examination of how different the image quality
assessment algorithms relate to the utility for the face recognition task. The
results of image quality assessment algorithms are further compared with those
of dedicated face image quality assessment algorithms. In total, 25 different
quality metrics are evaluated on three face image databases, BioSecure, LFW,
and VGGFace2 using three open-source face recognition solutions, SphereFace,
ArcFace, and FaceNet. Our results reveal a clear correlation between learned
image metrics to face image utility even without being specifically trained as
a face utility measure. Individual handcrafted features lack general stability
and perform significantly worse than general face-specific quality metrics. We
additionally provide a visual insight into the image areas contributing to the
quality score of a selected set of quality assessment methods.
