Publication Type

Conference Proceeding Article

Publication Date

10-2010

Abstract

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.

Keywords

cast analysis, multi-task learning, character identification, video summarization, face recognition

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Data Management and Analytics

Publication

MM '10: Proceedings of the 18th ACM International Conference on Multimedia: Firenze, Italy, October 25-29, 2010

First Page

831

Last Page

834

ISBN

9781605589336

Identifier

10.1145/1873951.1874090

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1145/1873951.1874090

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