1. LOGO FACE DETECTION APPLICATION Member: Vu Hoang Dung Vu Ha Linh Le Minh Tung Nguyen Duy Tan Chu Duy Linh Uong Thanh Ngoc CAPSTONE PROJECT Supervisor: Phan Duy Hung

2. FDA TEAM Contents Introduction 1 Plan 2 Requirements 33 Implementation 44 Conclusions 5

3. 1. Introduction  Existing Algorithm: FDA Team FDA TEAM Elastic Bunch Graph Matching (EBGM) 3-D Morphable Model. Boosting & Ensemble Solutions http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.71.97 50&rep=rep1&type=pdf http://www.mpi-inf.mpg.de/~blanz/html/data/morphmod2.pdf http://www.face-rec.org/algorithms/Boosting- Ensemble/16981346.pdf

4. 1. Introduction  Existing product: FDA Team FDA TEAM OpenCV – Intel’s Open Source Computer Vision initiative Face Tracking DLL from Camegie Mellon Real-time face detection program from FhG-II http://opencv.willowgarage.com/wiki/ http://chenlab.ece.cornell.edu/projects/FaceTrackin g/#Download http://www.iis.fraunhofer.de/bf/bv/ks/gpe/

5. 1. Introduction  Idea:  Develop an application to detect Face in Image  Fast speed  Reliable  Can integrated with other products FDA Team FDA TEAM

6. Objective System FDA Team FDA TEAM

7. 2. Plan 2.1 Roles and Responsibilities FDA Team FDA TEAM

8. 2. Plan 2.2 Software Process Model  Iterative Approach to Development FDA Team FDA TEAM

9. 2. Plan  System Requirement  Tool Requirement  Visual Studio 2008.  SQL Server 2008. .Net Framework 3.5.  Google code project site. FDA Team Operating System (OS)Hardware Microsoft Windows XP/ 7 (32 or 64 Bit) / Vista  1.5 GHz 32-bit (x86)/64-bit (x64) or higher  1 GB RAM (32-bit) or higher  2GB HDD free FDA TEAM

10. 3.1 Functional Requirements  User friendly - user can easily understand and handle in first use  Support small - big size image with different quality  Support format files: JPG, BMP, PNG, JPEG  Allows user to test the algorithms of image processing.  The processing must have a sequence as Image Original  Convert to HSV  Test H and V value of each pixel  Use 8 connected neighbor to find different regions  Identify region of face. FDA Team FDA TEAM

11. 3.2 Non-functional Requirements  The processing time of each function of image processing should be about 2 seconds  The result of searching face in images is processed less than 3 seconds  Time processing of searching a faces in the face database is not over 3 seconds FDA Team FDA TEAM

12. 4. Implementation 4.1 System Architectural Design FDA Team FDA TEAM

13. 4. Implementation 4.2 Component Diagram FDA Team FDA TEAM

14. 4. Implementation 1 Skin pixel classification 2 Connectivity analysis 3 Skin region identified is a face or not 4.3 Face Detection Algorithm FDA TEAM

15. 4. Implementation  Algorithm model process FDA Team Image original Convert from RGB to HSVHSV Test H and V value of each pixel Using Threshold Threshold Use 8 connected neighbor to find different regions Identify region of face FDA TEAM

16. 4. Implementation Original image FDA Team Image convert to HSV FDA TEAM Image convert to HSV with SoBel Operator Filter Blobs Draw edge around face

17. 4. Implementation Draw region found not filter in HSV image FDA Team Draw face detected after filter in HSV image FDA TEAM

18. 4. Implementation Binary Matrix FDA Team Histogram of image color All region’s information Face detected in original image FDA TEAM

19. 4. Implementation 4.4 Compare with other software FDA Team Test sample  Size: 42 images - 121 faces  14 images with 1 faces  13 images with 2 faces  15 images with more than 2 faces  Includes all kind of face: tilt head, obscure by other objects, half of face; in every kinds of light conditions; from low to high quality. Result:  Because FDA uses skin color to detect face, we can detect exactly above 70% of test sample with diversity faces. Other software dependent on eyes so detection's result is above 40%  Also because of that reason, FDA’s wrong ratio above 15% when its confusion with other skin area. While other software’s wrong ratio about 10% Test sample result FDA TEAM

20. 5. Conclusion 5.1 Advantages & Disadvantages  Advantages  Can handle High Definition Image  Completely open source, can develop in many ways.  Algorithm is fast and can be used in real-time applications.  Can detect all natural images under uncontrolled conditions.  Disadvantages  Black and white image – cannot detect skin  Contour distinguish  Confusion of human skin  Confusion of face form FDA Team FDA TEAM

21. 5. Conclusion 5.2 Implemented Technical Problems  Recently, threshold to detect face doesn’t has any research can perfectly detecting all faces.  Convert HSV can’t filter to remove all blobs.  Detect all skin area but can’t distinguish where that area contains eyes or not. 5.3 Solutions  Need more time to research about algorithm. FDA Team Cloud computing Using sample of eyes Low performance Face detect Wrong detection Calculate edge information FDA TEAM

22. 5. Conclusion Develop in Future Maintainability: Smart software like Neural network Performance: Cloud computing Availability: Code in C, C++ Reliability: Collect eyes sample FDA TEAM

23. Demo and Test Demo FDA FDA Team FDA TEAM

24. Q&A Question & Answer FDA Team FDA TEAM

25. LOGO FDA Team