We present an end-to-end system for detecting and clustering faces by identity in full-length movies. Unlike works that start with a predefined set of detected faces, we consider the end-to-end problem of detection and clustering together. We make three separate contributions. First, we combine a state-of-the-art face detector with a generic tracker to extract high quality face tracklets. We then introduce a novel clustering method, motivated by the classic graph theory results of Erdős and Renyi. It is based on the observations that large clusters can be fully connected by joining just a small fraction of their point pairs, while just a single connection between two different people can lead to poor clustering results. This suggests clustering using a verification system with very few false positives but perhaps moderate recall. We introduce a novel verification method, rank-1 counts verification, that has this property, and use it in a link-based clustering scheme. Finally, we define a novel end-to-end detection and clustering evaluation metric allowing us to assess the accuracy of the entire end-to-end system. We present state-of-the-art results on multiple video data sets and also on standard face databases.
With the development of multi-face tracking techniques, the problem of naming TV characters Another related problem is person re-identification @cite_32 @cite_46 @cite_6 in which the goal is to tell whether a person of interest seen in one camera has been observed by another camera. Re-identification typically uses the whole body on short time scales while naming TV characters focuses on faces, but over a longer period of time. has been also widely studied @cite_26 @cite_38 @cite_27 @cite_2 @cite_17 @cite_36 @cite_22 . Given precomputed face tracklets, the goal is to assign a name or an ID to a group of face tracklets with the same identity. @cite_17 @cite_36 iteratively cluster face tracklets and link clusters into longer tracks in a bootstrapping manner. @cite_22 train classifiers to find thresholds for joining tracklets in two stages: within a scene and across scenes. Similarly, we aim to generate face clusters in a fully unsupervised manner.
@cite_38 @cite_26 @cite_22 @cite_36 @cite_32 @cite_6 @cite_27 @cite_2 @cite_46 @cite_17
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We address the character identification problem in movies and television videos: assigning names to faces on the screen. Most prior work on person recognition in video assumes some supervised data such as screenplay or handlabeled faces. In this paper, our only source of ‘supervision’ are the dialog cues: first, second and third person references (such as “I'm Jack”, “Hey, Jack!” and “Jack left”). While this kind of supervision is sparse and indirect, we exploit multiple modalities and their interactions (appearance, dialog, mouth movement, synchrony, continuity-editing cues) to effectively resolve identities through local temporal grouping followed by global weakly supervised recognition. We propose a novel temporal grouping model that partitions face tracks across multiple shots while respecting appearance, geometric and film-editing cues and constraints. In this model, states represent partitions of the k most recent face tracks, and transitions represent compatibility of consecutive partitions. We present dynamic programming inference and discriminative learning for the model. The individual face tracks are subsequently assigned a name by learning a classifier from partial label constraints. The weakly supervised classifier incorporates multiple-instance constraints from dialog cues as well as soft grouping constraints from our temporal grouping. We evaluate both the temporal grouping and final character naming on several hours of TV and movies. Tracking multiple persons in a monocular video of a crowded scene is a challenging task. Humans can master it even if they loose track of a person locally by re-identifying the same person based on their appearance. Care must be taken across long distances, as similar-looking persons need not be identical. In this work, we propose a novel graph-based formulation that links and clusters person hypotheses over time by solving an instance of a minimum cost lifted multicut problem. Our model generalizes previous works by introducing a mechanism for adding long-range attractive connections between nodes in the graph without modifying the original set of feasible solutions. This allows us to reward tracks that assign detections of similar appearance to the same person in a way that does not introduce implausible solutions. To effectively match hypotheses over longer temporal gaps we develop new deep architectures for re-identification of people. They combine holistic representations extracted with deep networks and body pose layout obtained with a state-of-the-art pose estimation model. We demonstrate the effectiveness of our formulation by reporting a new state-of-the-art for the MOT16 benchmark. The code and pre-trained models are publicly available. We describe a probabilistic method for identifying characters in TV series or movies. We aim at labeling every character appearance, and not only those where a face can be detected. Consequently, our basic unit of appearance is a person track (as opposed to a face track). We model each TV series episode as a Markov Random Field, integrating face recognition, clothing appearance, speaker recognition and contextual constraints in a probabilistic manner. The identification task is then formulated as an energy minimization problem. In order to identify tracks without faces, we learn clothing models by adapting available face recognition results. Within a scene, as indicated by prior analysis of the temporal structure of the TV series, clothing features are combined by agglomerative clustering. We evaluate our approach on the first 6 episodes of The Big Bang Theory and achieve an absolute improvement of 20 for person identification and 12 for face recognition. Identification of characters in films, although very intuitive to humans, still poses a significant challenge to computer methods. In this paper, we investigate the problem of identifying characters in feature-length films using video and film script. Different from the state-of-the-art methods on naming faces in the videos, most of which used the local matching between a visible face and one of the names extracted from the temporally local video transcript, we attempt to do a global matching between names and clustered face tracks under the circumstances that there are not enough local name cues that can be found. The contributions of our work include: 1) A graph matching method is utilized to build face-name association between a face affinity network and a name affinity network which are, respectively, derived from their own domains (video and script). 2) An effective measure of face track distance is presented for face track clustering. 3) As an application, the relationship between characters is mined using social network analysis. The proposed framework is able to create a new experience on character-centered film browsing. Experiments are conducted on ten feature-length films and give encouraging results.