  COVID-19 pandemic and social distancing urge a reliable human face
recognition system in different abnormal situations. However, there is no
research which studies the influence of glass factor in facial recognition
system. This paper provides a comprehensive review of glass factor. The study
contains two steps: data collection and accuracy test. Data collection includes
collecting human face images through different situations, such as clear
glasses, glass with water and glass with mist. Based on the collected data, an
existing state-of-the-art face detection and recognition system built upon
MTCNN and Inception V1 deep nets is tested for further analysis. Experimental
data supports that 1) the system is robust for classification when comparing
real-time images and 2) it fails at determining if two images are of same
person by comparing real-time disturbed image with the frontal ones.
