Conference Proceeding Article
We investigate the problem of automatically identifying characters in a movie with the supervision of actor-character name correspondence provided by the movie cast. Our proposed framework, namely Cast2Face, is featured by: (i) we restrict the names to assign within the set of character names in the cast; (ii) for each character, by using the corresponding actor's name as a key word, we retrieve from Google image search a group of face images to form the gallery set; and (iii) the probe face tracks in the movie are then identified as one of the actors by robust multi-task joint sparse representation and classification method. The assigned actor name on a face track is then mapped to the character name based on the cast again. In addition to face naming, we further apply the proposed method to spotlights summarization of a particular actor in his/her movies. Empirical evaluations on several feature-length movies demonstrate the satisfying performance of our method.
cast analysis, multi-task learning, character identification, video summarization, face recognition
Databases and Information Systems | Graphics and Human Computer Interfaces
Data Management and Analytics
MM '10: Proceedings of the 18th ACM International Conference on Multimedia: Firenze, Italy, October 25-29, 2010
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XU, Mengdi; YUAN, Xiaotong; SHEN, Jialie; and YAN, Shuicheng.
Cast2Face: Character identification in movie with actor-character correspondence. (2010). MM '10: Proceedings of the 18th ACM International Conference on Multimedia: Firenze, Italy, October 25-29, 2010. 831-834. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/651
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