Title: Automatic Face Image Quality Prediction,
Abstract: 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.