Robust face name graph matching for movie character identification - Final PPT
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The document proposes a robust face name graph matching system for movie character identification. It involves detecting faces accurately using clustering, representing them in a graph, and matching faces using an edit-operation based graph matching algorithm. The system aims to identify characters in low resolution or complex backgrounds. It analyzes existing limitations and proposes a two-scheme approach using external script resources and original graphs. The system architecture involves modules for login/authentication, detection, training, and recognition. It concludes the proposed schemes improve clustering and identification of faces from movies and discusses future work on optimal functions for genres and exploiting more character relationships.
Robust face name graph matching for movie character identification - Final PPT
- 1. ROBUST FACE NAME GRAPH MATCHING FOR MOVIE CHARACTER IDENTIFICATION
- 2. AGENDA • • • • • • • • • 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
- 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.
- 17. DESIGN PAGE
- 18. LOGIN PAGE
- 19. IMAGE INSERTION
- 20. TRAINING DATASET
- 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.