A Comparative Analysis of the Face Recognition Methods in Video Surveillance Scenarios

TL;DR

This paper compares various face recognition methods in video surveillance scenarios, evaluating their performance under different conditions using a standardized dataset to identify the most effective solutions.

Contribution

It provides a benchmark comparison of face recognition methods with a unified backbone architecture, focusing on their effectiveness in real-world surveillance conditions.

Findings

Best methods vary for masked and non-masked faces

High intra-class variance impacts recognition accuracy

Constructed a diverse surveillance face dataset for evaluation

Abstract

Facial recognition is fundamental for a wide variety of security systems operating in real-time applications. In video surveillance based face recognition, face images are typically captured over multiple frames in uncontrolled conditions; where head pose, illumination, shadowing, motion blur and focus change over the sequence. We can generalize that the three fundamental operations involved in the facial recognition tasks: face detection, face alignment and face recognition. This study presents comparative benchmark tables for the state-of-art face recognition methods by testing them with same backbone architecture in order to focus only on the face recognition solution instead of network architecture. For this purpose, we constructed a video surveillance dataset of face IDs that has high age variance, intra-class variance (face make-up, beard, etc.) with native surveillance facial imagery data for evaluation. On the other hand, this work discovers the best recognition methods for different conditions like non-masked faces, masked faces, and faces with glasses.