International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020


IJSTR >> Volume 2- Issue 7, July 2013 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Face And Name Matching In A Movie By Grapical Methods In Dynamic Way

[Full Text]



Ishwarya, Madhu B, Veena Potdar



Key Words: Graph matching, Graph Partition, Face Tracking, Face clustering, error correction graph matching, indexing, skimming.



Abstract: With the flourishing development of the movie industry, a huge amount of movie data is being generated every day. It becomes very important for a media creator or distributor to provide better media content description, indexing and organization, so that users can easily browsing, skimming and retrieving the content of interest. Our goal is to automatically determine the cast of a feature-length film and match it with the character name. This is challenging because the cast size is not known, with appearance changes of faces caused by extrinsic imaging factors like illumination, pose, and expression often greater than due to differing identities. Although in the existing system the performances are limited due to the noises generated during the face tracking and face clustering process. The contributions of this work include: A noise insensitive character relationship. An error correcting graph matching algorithm is introduced. Complex character changes are handled simultaneously by graph partition and graph matching.



[1]. Y. Zhang, C. Xu, H. Lu, and Y. Huang, “Character identification in feature-length films using global face-name matching,”

[2]. M. Everingham, J. Sivic, and A. Zissserman, “Taking the bite out of automated naming of characters in tv video,” .

[3]. C. Liang, C. Xu, J. Cheng, and H. Lu, “Tvparser: An automatic tv video parsing method,” .

[4]. T. Cour, B. Sapp, C. Jordan, and B. Taskar, “Learning from ambiguously labelled images,” .

[5]. J. Stallkamp, H. K. Ekenel, and R. Stiefelhagen, “Video-based face recognition on real-world data.”

[6]. O. Arandjelovic and R. Cipolla, “Automatic cast listing in feature-length films with anisotropic manifold space,”