Discovering Human Characteristic using Face Analysis. our g

1. Discovering Human Features Using Face Analysis Supervised By: Dr.EL-SayedFouadRadwan

3. System Purpose.

4. Where ?

6. Design Application and database Design.

7. Implementation.

8. Testing.3) Future applicable extensions.

9. introduction

10. Introduction : Facial physiognomy. Your face is the mirror of your personality. Statistical results has shown that there is a strong connection between facial features and personality traits. Since thousands of years people have been trying to study the relationship between facial features and personality traits.

12. Introduction : Project Life Cycle Input image Database of Features Store Statistical data Face Detection Features Extraction FFT Human Face Statistical data Features Discovering Human Characteristics

14. Java: Application Logic and Neural Network.

15. MySQL: Back end database (Statistical Data & characteristics) .

16. C#.net: Front end .

17. IDEs: Netbeans 6.9.1, Visual Studio 2010.

19. Planning : ● We believe that the best way to get a good working application is to give enough time for planning and designing. ● In our planning phase, we passed through the following….

21. Intel processor is recommended for better performance.

22. Minimum system requirements:

23. 1.6 GHz processor

24. 2. 256 MB RAM

25. 3. 150 MB free disk space

26. S/W requirements:

27. Windows Operating System.

28. MySQL Server.

29. Java Compiler.

30. .NET Framework.

31. Who use The System:

32. Psychiatrists.

33. Persons interested in Face Analysis.

35. Application & Database Design

37. Database Module

38. Classification Module

42. View Module: Develop easy and Simple user interface that load image And automatic classify then the result will be the characteristic of face

43. The Environment

44. Environment: Windows We use windows as base operating system for Our project because of using of C#.net, and most of people use windows as native Operating System

46. Platform independent

47. Secure

48. Simple

49. Multi-Threaded

50. Distributed

51. Has good libraries in neural network like (*****) that We used in our classification.

53. Platform dependent.

54. Has good libraries in Image Processing like(Aforg.Net – OpenCV- Luxand) that we use in Extract Face Features

55. Environment: MySQL • Relational database management system (RDBMS) • Providing multiuser access to a number of databases • Works on many different system platforms

56. Implementation

57. View : C#.net Step 1: Load image to the Application.

58. View : C#.net Step 2: Detect Face Features.

59. View : C#.net Step 3: Describe Human Characteristics.

60. Application Logic: java Implementation of neural network that classify Features

61. Future Extensions

63. Project developed as web Application.

66. Thanks Thanks Team Members: C# Developer: Hanan El-Hussiny. PHP Developer: Mohamed IbrahemAtwa Java Developer:RababTalaat. Graphic designer:SafayaAbdEl-Wahab. Java Developer:Ahmed Salah. Team Leader: Mohamed Adel Shahpoup Email: egydev.java@gmail.com Mobile:0142784643. MyBlog:http://www.javakick.wordpress.com