1. ROBUST FACE NAME GRAPH
MATCHING FOR MOVIE
CHARACTER IDENTIFICATION

2. AGENDA
•
•
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•
•
•
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Abstract
Existing system
Proposed system
System architecture
List of modules
Module description
Screen shot
Conclusion
Future Enhancement

3. ABSTRACT
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.

4. EXISTING SYSTEM
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. PROPOSED SYSTEM
By using clustering mechanism, the face of the
movie character is detected more accurately.
TECHNOLOGY USED
• 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.

6.  Second, The original graph is employed for face
name graph representation.
ADVANTAGES
 In the proposed system, the face detection is
performed in a minute process.
 The
faces
are
identified
easily
resolution, complex background also.
in
low

7. SYSTEM ARCHITECTURE

8. HARDWARE REQUIREMENTS
• System
:
Pentium IV 2.4 GHz.
• Hard Disk
:
40 GB.
• Monitor
:
15 VGA Colour.
• Mouse
:
Logitech.
• Ram
:
512 Mb.

9. SOFTWARE REQUIREMENTS
• Operating System
:
Windows XP
• Front End
:
Visual Studio 2008
• Back End
:
Ms-Sql Server

10. LIST OF MODULES
• Login and authentication module
• Detection module
• Training module
• Recognition module

11. Login & Authentication Module
• The Robust Face-Name Graph Matching for Movie
Character Identification designing and how we going
to do face detection and recognition in the project.
• The images will explain about the facial fetching
details.
• After that admin going to login with the details which
needed for the login page.

12. Detection Module
• In this module, the face of the movie character is
detected.
• We are using the emgucv library for detection and it
is installed for adding references.
• When you will complete the references you will get
the emgu controls in the toolbox.

13. Training Module
• In this module, I’m going to train the faces which are
detected in the earlier module.
• The user can train the system by adding the names of
the user.
• The name of the training data set is stored in image
format with the graph name.

14. Recognition Module
• This module going to recognize the face of the movie
characters which is we previously stored on the face
database.
• We just found that the give the real name of it. This
is going to be done here.
• Here we are using the With the help of these
eigenObjectRecognizer we are going to recognize the face.

15. DATA FLOW DIAGRAM

16. SCREEN SHOTS
DESIGN PAGE

17. DESIGN PAGE

18. LOGIN PAGE

19. IMAGE INSERTION

20. TRAINING DATASET

21. TRAINING AND RECOGNITION

22. DETECTION MODULE

23. CONCLUSION
•
The proposed two schemes are useful to improve results for
clustering and identification of the face tracks extracted from
uncontrolled movie videos.
•
From the sensitivity analysis, also shown that to some
degree, such schemes have better robustness to the noises in
constructing affinity graphs than the traditional methods.
• A third conclusion is a principle for developing robust character
identification method intensity alike noises must be emphasized
more than the coverage alike noises.

24. FUTURE ENHANCEMENT
• In the future, we will extend our work to investigate
the optimal functions for different movie genres.
Another goal of future work is to exploit more
character relationships, e.g., the sequential statistics
for the speakers, to build affinity graphs and improve
the robustness.

25. REFERENCE PAPER
• J. Sang, C. Liang, C. Xu, and J. Cheng, “Robust
movie character identification and the sensitivity
analysis,” in ICME, 2011, pp. 1–6.
• H. Bunke, “On a relation between graph edit
distance and maximum common sub graph,”
Pattern Recognition Letters, vol. 18, pp. 689–694

26. REFERENCE PAPER - Contd
• M. Everingham and A. Zisserman, “Identifying
individuals in video by combining
”generative” and discriminative head models,”
in ICCV,2005, pp. 1103–1110.