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In this paper proposes a global face-name graph matching based framework for robust movie character identification. The proposed system two schemes are considered. There are connections as well as differences between them. Regarding the connections, the proposed two schemes both belong to the global matching based category, where external script resources are utilized. This research study to improve the robustness, the ordinal graph is employed for face and name graph representation and a novel graph matching algorithm called Error Correcting Graph Matching (ECGM) is introduced. Regarding the differences, scheme 1 sets the number of clusters when performing face clustering .The face graph is restricted to have identical number of vertexes with the name graph. While, in scheme 2, no cluster number is required and face tracks are clustered based on their intrinsic data structure. Auto face identification of characters in films has drawn most research interests and led to many interesting applications. Since huge variation in the appearance of each character is found, it is a challenging problem. Existing methods evaluates promising results in clean environment, the performances are limited in complex movie scenes due to the noises generated during the face tracking and face clustering process. This study presents two schemes of global face-name matching based framework for robust character identification. In this paper main contributions of this study include the first noise insensitive character relationship representation is incorporate, next study introduces an edit operation based graph matching algorithm, next complex character changes are handled by simultaneously graph partition and graph matching and beyond existing character identification approaches. The proposed schemes demonstrate state-of-the-art performance on movie character identification in various movies.

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How to Cite
.S.Jagadeesan, & C.Gowthaman. (2017). An effective character multi role identification model using krelation graph clustering in visual image datasets . International Journal of Intellectual Advancements and Research in Engineering Computations, 5(2), 1423–1430. Retrieved from