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FACE DETECTION APPLICATION

FACE DETECTION APPLICATION

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FACE DETECTION APPLICATION

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

  2. Introduction Conclusions 1 5 Plan 2 Requirements 3 3 Implementation 4 4 Contents

  3. FDA Team 1. Introduction • Existing Algorithm: Elastic Bunch Graph Matching (EBGM) • 3-D Morphable Model. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.71.9750&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 • Boosting & Ensemble Solutions

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

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

  6. FDA Team Objective System

  7. FDA Team 2. Plan 2.1 Roles and Responsibilities

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

  9. FDA Team 2. Plan • System Requirement • Tool Requirement • Visual Studio 2008. • SQL Server 2008. • .Net Framework 3.5. • Google code project site.

  10. FDA Team 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.

  11. FDA Team 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

  12. FDA Team 4. Implementation 4.1 System Architectural Design

  13. FDA Team 4. Implementation 4.2 Component Diagram

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

  15. FDA Team 4. Implementation • Algorithm model process

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

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

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

  19. FDA Team 4. Implementation 4.4 Compare with other software • Testsample • 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

  20. FDA Team 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

  21. FDA Team 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.

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

  23. FDA Team Demo and Test Demo FDA

  24. FDA Team Q&A Question & Answer

  25. Thank You ! FDA Team