The recognition performance of biometric systems strongly depends on the
quality of the compared biometric samples. Motivated by the goal of
establishing a common understanding of face image quality and enabling system
interoperability, the committee draft of ISO/IEC 29794-5 introduces expression
neutrality as one of many component quality elements affecting recognition
performance. In this study, we train classifiers to assess facial expression
neutrality using seven datasets. We conduct extensive performance benchmarking
to evaluate their classification and face recognition utility prediction
abilities. Our experiments reveal significant differences in how each
classifier distinguishes "neutral" from "non-neutral" expressions. While Random
Forests and AdaBoost classifiers are most suitable for distinguishing neutral
from non-neutral facial expressions with high accuracy, they underperform
compared to Support Vector Machines in predicting face recognition utility.

鉴于人脸图像质量对生物特征识别系统的识别性能有很大影响，本研究旨在训练分类器来评估人脸表情中性，通过在七个数据集上进行大量性能基准测试，我们揭示了每个分类器在将 “中性” 与 “非中性” 表情区分开来方面存在显著差异，随机森林和 AdaBoost 分类器在高准确性下最适合区分中性与非中性表情，但在预测面部识别效用方面却表现不及支持向量机。

高效表情中立性估计及其应用于人脸识别效用预测

Efficient Expression Neutrality Estimation with Application to Face  Recognition Utility Prediction

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。