融合生物特征与外观信息，提升队列中人脸追踪精度。
FaceQSORT: a Multi-Face Tracking Method based on Biometric and Appearance Features
- 结合生物特征与外观特征进行人脸关联
- 在7个序列12730帧上超越现有追踪方法
- 适合排队场景下的人脸追踪研究
本文提出一种新型多人脸追踪方法FaceQSORT，旨在解决部分遮挡或侧脸等挑战。该方法将同一图像区域提取的生物特征与视觉外观特征相结合，实现更可靠的关联。名称中的'Q'代表设计场景：人们排队通过闸机时的人脸追踪。为此构建了新数据集'Paris Lodron University Salzburg Faces in a Queue'，包含7个完整标注的高难度序列（共12730帧），并联合两个公开数据集进行评估。实验表明，FaceQSORT在该场景下优于现有最优追踪器。进一步实验分析了参数选择、相似度度量及人脸识别模型对结果的影响。
原文摘要 · Abstract (English)
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).
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