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Face tracking serves as the crucial initial step in mobile applicationstrying to analyse target faces over time in mobile settings. However, thisproblem has received little attention, mainly due to the scarcity of dedicatedface tracking benchmarks. In this work, we introduce MobiFace, the firstdataset for single face tracking in mobile situations. It consists of 80unedited live-streaming mobile videos captured by 70 different smartphone usersin fully unconstrained environments. Over $95K$ bounding boxes are manuallylabelled. The videos are carefully selected to cover typical smartphone usage.The videos are also annotated with 14 attributes, including 6 newly proposedattributes and 8 commonly seen in object tracking. 36 state-of-the-arttrackers, including facial landmark trackers, generic object trackers andtrackers that we have fine-tuned or improved, are evaluated. The resultssuggest that mobile face tracking cannot be solved through existing approaches.In addition, we show that fine-tuning on the MobiFace training datasignificantly boosts the performance of deep learning-based trackers,suggesting that MobiFace captures the unique characteristics of mobile facetracking. Our goal is to offer the community a diverse dataset to enable thedesign and evaluation of mobile face trackers. The dataset, annotations and theevaluation server will be on \url{https://mobiface.github.io/}.

MobiFace: A Novel Dataset for Mobile Face Tracking in the Wild