Face image quality can be defined as a measure of the utility of a face image
to automatic face recognition. In this work, we propose (and compare) two
methods for automatic face image quality based on target face quality values
from (i) human assessments of face image quality (matcher-independent), and
(ii) quality values computed from similarity scores (matcher-dependent). A
support vector regression model trained on face features extracted using a deep
convolutional neural network (ConvNet) is used to predict the quality of a face
image. The proposed methods are evaluated on two unconstrained face image
databases, LFW and IJB-A, which both contain facial variations with multiple
quality factors. Evaluation of the proposed automatic face image quality
measures shows we are able to reduce the FNMR at 1% FMR by at least 13% for two
face matchers (a COTS matcher and a ConvNet matcher) by using the proposed face
quality to select subsets of face images and video frames for matching
templates (i.e., multiple faces per subject) in the IJB-A protocol. To our
knowledge, this is the first work to utilize human assessments of face image
quality in designing a predictor of unconstrained face quality that is shown to
be effective in cross-database evaluation.

通过支持向量回归模型与卷积神经网络提取的人脸特征预测人脸图像质量，利用人类对人脸图像质量的评估创建跨库评估的人脸图像预测器，在减少 FNMR 方面表现出极高的效果，是第一个成功应用人工评估的跨库人脸质量预测研究。