Performance analysis of facial recognition: A critical review through glass factor

TL;DR

This paper investigates how glasses affect facial recognition accuracy, revealing robustness in classification but challenges in identity verification when glasses distort images.

Contribution

It provides a novel analysis of the glass factor's impact on facial recognition systems using real-world disturbed images.

Findings

System is robust for classification with real-time images.

Fails to verify identity when comparing disturbed and frontal images.

Highlights need for improved recognition under glass-related distortions.

Abstract

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.