Performance analysis of facial recognition: A critical review through glass factor
This addresses a reliability issue for facial recognition systems in abnormal conditions like pandemics, but it is incremental as it applies an existing method to new data.
The paper tackled the problem of facial recognition performance under the 'glass factor' (e.g., glasses with water or mist) by testing an existing MTCNN and Inception V1 system, finding it robust for classification but failing at identity verification with disturbed images.
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.