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Considering challenges such as occlusions due to face coverings, ocular biometrics have risen as a potential remedy. Visible light ocular modalities use features extracted from in and around the eyes from 'selfie' captures for personal identification. A less-visited ocular neighbourhood is the eyebrows region. Previous works on eyebrows have been mostly using subject-specific verification protocols where identities overlap between the training and test sets and do not directly address the issue of doppelgangers and twins. Here, the evaluation of five deep learning models, lightCNN, ResNet, DenseNet, MobileNetV2, and SqueezeNet, for eyebrow-based user authentication in a subject independent environment across different data sets, lighting conditions, resolutions, and facial expressions is done. Also a challenging simulated identical twins scenario in our training and testing data sets is presented. Our results show a 4.2% equal error rate (EER) and 0.993 area under the curve (AUC) on a subset of the FACES data set (154 subjects) and 6.8% EER, 0.978 AUC on a subset of the VISOB data set (350 subjects). Despite achieving promising accuracy during short-term evaluations, the proposed methods showed lower accuracy when matching against long-term data or when facing data captured under varying illumination and facial expressions.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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