A Cluster-Matching-Based Method for Video Face Recognition

TL;DR

This paper introduces a scalable video face recognition method using unsupervised clustering and cluster matching, achieving high accuracy and capable of identifying unregistered faces.

Contribution

It presents a novel cluster-matching heuristic for face recognition in videos that improves scalability and can detect non-registered individuals.

Findings

Recall of 99.435% in face recognition

Precision of 99.131% in face recognition

Capable of identifying non-registered persons

Abstract

Face recognition systems are present in many modern solutions and thousands of applications in our daily lives. However, current solutions are not easily scalable, especially when it comes to the addition of new targeted people. We propose a cluster-matching-based approach for face recognition in video. In our approach, we use unsupervised learning to cluster the faces present in both the dataset and targeted videos selected for face recognition. Moreover, we design a cluster matching heuristic to associate clusters in both sets that is also capable of identifying when a face belongs to a non-registered person. Our method has achieved a recall of 99.435% and a precision of 99.131% in the task of video face recognition. Besides performing face recognition, it can also be used to determine the video segments where each person is present.