Ocular biometrics is attracting exceeding attention from research community and industry alike thanks to its accuracy, security, and ease of use in mobile devices, especially in the presence of occlusions such as masks worn during the COVID-19 pandemic. When considering the extended periocular region, eyebrows have not been getting enough attention due to their perceived low uniqueness. In this paper, we evaluate a mobile-friendly deep-learning model for eyebrow-based user authentication. Specifically, we used a fine-tuned lightCNN model for eyebrow based user authentication with promising results on a particularly challenging dataset and evaluation protocol (open-set with simulated twins). The methods achieved 0.99 AUC and 4.3% EER in VISOB dataset and 0.98 AUC and 5.6% EER on SiW datasets using closed-set and open-set analysis, respectively.