Adaptive Regression Splines Models for Predicting Facial Image Verification and Quality Assessment Scores

Abstract

— Many biometric applications are faced with enormous performance challenges due to submission of low quality facial images. In this study, adaptive regression splines (ARES) models were built for predicting algorithm matching scores (AMS) and overall quality scores (OQS). A face verification and image quality assessment (FVIQA) framework was adopted to extract five facial quality features from still images. The SCface database was adopted for the training and testing datasets with 2,093 and 897 images respectively. ARES models were built from the normalized individual quality scores and algorithm matching scores using ARESLab in the MATLAB environment. A black face surveillance camera (BFSC) database of 50 subjects was populated to mimic the SCface database and act as the target dataset for the model validation. Results from the study shows that FVIQA quality scores and other experimental results are comparable and consistent with previous research works. The model ANOVA decomposition showed that pose variation is the major determinant for model OQS and AMS with 0.046 and 0.261 respectively. From the performance evaluation, model OQS achieved 99.96% and 99.81% prediction accuracy on the test and target datasets while model AMS achieved 87.04% and 84.73% respectively. Subsequently, no failure-to-acquire (FTA) was recorded when superior face images were selected from the SCface database using the developed image verification and quality assessment (IVQA) number