FaceQSORT: a Multi-Face Tracking Method based on Biometric and Appearance Features

TL;DR

FaceQSORT is a new multi-face tracking method that combines biometric and appearance features to improve tracking accuracy in queue scenarios, validated on a new dataset and outperforming existing methods.

Contribution

The paper introduces FaceQSORT, a novel multi-face tracking approach that integrates biometric and visual features, and provides a new dataset for queue scenario face tracking.

Findings

FaceQSORT outperforms state-of-the-art trackers in queue scenarios.

The new dataset 'Paris Lodron University Salzburg Faces in a Queue' is publicly available.

Comprehensive experiments evaluate parameter effects and biometric feature choices.

Abstract

In this work, a novel multi-face tracking method named FaceQSORT is proposed. To mitigate multi-face tracking challenges (e.g., partially occluded or lateral faces), FaceQSORT combines biometric and visual appearance features (extracted from the same image (face) patch) for association. The Q in FaceQSORT refers to the scenario for which FaceQSORT is desinged, i.e. tracking people's faces as they move towards a gate in a Queue. This scenario is also reflected in the new dataset `Paris Lodron University Salzburg Faces in a Queue', which is made publicly available as part of this work. The dataset consists of a total of seven fully annotated and challenging sequences (12730 frames) and is utilized together with two other publicly available datasets for the experimental evaluation. It is shown that FaceQSORT outperforms state-of-the-art trackers in the considered scenario. To provide a deeper insight into FaceQSORT, comprehensive experiments are conducted evaluating the parameter selection, a different similarity metric and the utilized face recognition model (used to extract biometric features).