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).

翻译：本研究提出了一种名为FaceQSORT的新型多面部追踪方法。为应对多面部追踪中的挑战（如部分遮挡或侧面人脸），FaceQSORT结合了从同一图像（人脸）区域提取的生物特征与视觉外观特征进行关联。FaceQSORT中的"Q"指代该方法所设计的应用场景，即追踪人员在队列中向闸门移动时的面部。该场景亦体现在本工作公开的新数据集"巴黎洛德隆大学萨尔茨堡队列人脸数据集"中。该数据集共包含七段完整标注且具有挑战性的序列（12730帧），并与另外两个公开数据集共同用于实验评估。实验表明，在当前研究场景下，FaceQSORT的性能优于现有先进追踪器。为深入解析FaceQSORT，本研究通过参数选择、不同相似性度量及所用面部识别模型（用于提取生物特征）的评估进行了全面实验。