ROBUST FACE NAME GRAPH MATCHING FOR MOVIE

CHARACTER IDENTIFICATION

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

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.

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.

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.

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.

SYSTEM ARCHITECTURE

HARDWARE REQUIREMENTS

• System : Pentium IV 2.4 GHz.

• Hard Disk : 40 GB.

• Monitor : 15 VGA Colour.

• Mouse : Logitech.

• Ram : 512 Mb.

SOFTWARE REQUIREMENTS

• Operating System : Windows XP

• Front End : Visual Studio 2008

• Back End : Ms-Sql Server

LIST OF MODULES

• Login and authentication module

• Detection module

• Training module

• Recognition module

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.

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.

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.

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.

DATA FLOW DIAGRAM

SCREEN SHOTSDESIGN PAGE

DESIGN PAGE

LOGIN PAGE

IMAGE INSERTION

TRAINING DATASET

TRAINING AND RECOGNITION

DETECTION MODULE

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.

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.

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

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.

QUERI

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