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

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

1. Introduction  Existing Algorithm: FDA Team FDA TEAM Elastic Bunch Graph Matching (EBGM) 3-D Morphable Model. Boosting & Ensemble Solutions 50&rep=rep1&type=pdf Ensemble/ pdf

1. Introduction (cont.)  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 g/#Download

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

Objective System FDA Team FDA TEAM

2. Plan 2.1 Roles and Responsibilities FDA Team FDA TEAM

2. Plan (cont.) 2.2 Software Process Model  Iterative Approach to Development FDA Team FDA TEAM

3.1 Functional Requirements  Can open all image files: JPG, BMP, PNG, JPEG  Detect from small to big size image with different quality  Can detect exactly at least 70% of images.  Show all detected faces in the inputted image.  Show detected faces’ information.  Represent original image in binary matrix.  Represent original image in black-white color after remove all blobs.  Show all found regions and its information can be save into file.  Draw color’s deep histogram of inputted image.  Add tag a detected face into database.  Edit person’s information.  Search image by tag in database.  Delete tag, delete person in database. FDA Team FDA TEAM

3.2 Non-functional Requirements  Detecting time for each image has size less than 1MB is about 1mins.  Show processing time.  The processing time of each others function of image processing should be about 2 seconds  The result of searching face in images is processed less than 3 seconds  User friendly - user can easily understand and handle in first use  Have user guide.  Send feedback to project team. FDA Team FDA TEAM

4. Implementation 4.1 System Architectural Design FDA Team FDA TEAM

4. Implementation (cont.) 4.2 Component Diagram FDA Team FDA TEAM

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

4. Implementation (cont.)  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

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

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

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

4. Implementation (cont.) 4.4 Compare with other software FDA Team Test sample  Size: 90 images faces  30 images with 1 faces  30 images with 2 faces  30 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

4. Implementation (cont.) 4.5 Test Plan  Interface  Show full labels, textboxes, buttons, list views… and locate in right location like design interface.  All icons have to be appropriate, interactive with its action.  Performance of detecting 1 image less than 1MB about < 1min and other functions is < 3s  Algorithm  Test result must be above 70% exactly for detecting 100 images in test sample.test sample  Convert HSV algorithm must convert and remove all blobs 90% exactly.  Result after filter must remove all regions not satisfy golden ratio.  Show all found regions’ information.  Other functions  Can add 1 or many tags for 1 person.  Search tag result have to be shown what user inputted exactly.  Can update all person’s information.  Can delete a tag or for a person from database. FDA TEAM Test case document

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

5. Conclusion (cont.) 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

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

Demo and Test Demo FDA FDA Team FDA TEAM

Q&A Question & Answer FDA Team FDA TEAM

LOGO FDA Team