The document presents two schemes for robust character identification in movies using global face name matching frameworks. The schemes incorporate a noise insensitive character relationship representation and introduce an edit operation based graph matching algorithm to identify faces in video and label them with character names from the cast list despite variations in each character's appearance. This helps enable semantic movie indexing and retrieval.

2. Automatic face identification of characters in
movies become a challenging problem due to the
huge variation of each characters. In this paper, we
present two schemes of global face name matching
based frame work for robust character identification.
A noise insensitive character relationship
representation is incorporated. We introduce an edit
operation based graph matching algorithm.

3. The objective is to identify the faces of the characters in the
video and label them with the corresponding names in the
cast.
The textual clues like cast list, scripts, subtitles and closed
captions are usually exploited.
This occurrences provides lots of movie structure and
content.
Automatic character identification is essential for semantic
movie index and retrieval, scene segmentation and other
applications.

4. During face tracking and face clustering process,
the noises has been generated.
The performance are limited at the time of noise
generation.
DISADVANTAGES
 The time taken for detecting the face is too long.
 The detected face cannot be more accurate.

5. By using clustering mechanism, the face of the
movie character is detected more accurately.
ADVANTAGES
 In the proposed system, the face detection is
performed in a minute process.
 The faces are identified easily in low
resolution, complex background also.

7.  Two schemes considered in robust face name graph
matching algorithm
 First, External script resources are utilized in both
schemes belong to the global matching based
category.
 Second, The original graph is employed for face
name graph representation.

8.  In ECGM, the difference between the two graph is
measured by edit distance which is a sequence of
graph edit operation.
 The optimal match is achieved with the least edit
distance.

9. FRONT END : Visual Studio 2010
BACK END: C#.Net

10. Design
Detection
Recognition