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This paper investigates long-term face tracking of a specific person givenhis/her face image in a single frame as a query in a video stream. Throughtaking advantage of pre-trained deep learning models on big data, a novelsystem is developed for accurate video face tracking in the unconstrainedenvironments depicting various people and objects moving in and out of theframe. In the proposed system, we present a detection-verification-trackingmethod (dubbed as 'DVT') which accomplishes the long-term face tracking taskthrough the collaboration of face detection, face verification, and(short-term) face tracking. An offline trained detector based on cascadedconvolutional neural networks localizes all faces appeared in the frames, andan offline trained face verifier based on deep convolutional neural networksand similarity metric learning decides if any face or which face corresponds tothe queried person. An online trained tracker follows the face from frame toframe. When validated on a sitcom episode and a TV show, the DVT methodoutperforms tracking-learning-detection (TLD) and face-TLD in terms of recalland precision. The proposed system is also tested on many other types of videosand shows very promising results.

Long-term face tracking in the wild using deep learning