Efficient Expression Neutrality Estimation with Application to Face Recognition Utility Prediction
This work addresses the need for standardized face image quality assessment to improve biometric system interoperability, though it is incremental as it builds on existing ISO/IEC standards and classifier methods.
The study tackled the problem of assessing facial expression neutrality as a quality element for face recognition systems by training classifiers on seven datasets, finding that Random Forests and AdaBoost achieved high accuracy in classification but underperformed compared to Support Vector Machines in predicting recognition utility.
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.