robust face name graph matching for movie character identification - final ppt
TRANSCRIPT
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ROBUST FACE NAME GRAPH MATCHING FOR MOVIE
CHARACTER IDENTIFICATION
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AGENDA• Abstract• Existing system• Proposed system• System architecture• List of modules• Module description• Screen shot• Conclusion• Future Enhancement
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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.
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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.
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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.
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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 in low resolution,
complex background also.
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SYSTEM ARCHITECTURE
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HARDWARE REQUIREMENTS
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Monitor : 15 VGA Colour.
• Mouse : Logitech.
• Ram : 512 Mb.
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SOFTWARE REQUIREMENTS
• Operating System : Windows XP
• Front End : Visual Studio 2008
• Back End : Ms-Sql Server
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LIST OF MODULES
• Login and authentication module
• Detection module
• Training module
• Recognition module
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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.
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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.
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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.
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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.
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DATA FLOW DIAGRAM
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SCREEN SHOTSDESIGN PAGE
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DESIGN PAGE
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LOGIN PAGE
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IMAGE INSERTION
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TRAINING DATASET
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TRAINING AND RECOGNITION
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DETECTION MODULE
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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.
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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.
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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
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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.
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QUERI
ES