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FACE DETECTION APPLICATION
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
1. Introduction Existing Algorithm:
FDA TeamFDA TEAM
Elastic Bunch Graph Matching (EBGM)3-D Morphable Model.
Boosting & Ensemble Solutions
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.71.9750&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
1. Introduction Existing product:
FDA TeamFDA 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/FaceTracking/#Download
http://www.iis.fraunhofer.de/bf/bv/ks/gpe/
1. Introduction
Idea: Develop an application to detect Face in Image Fast speed Reliable Can integrated with other products
FDA TeamFDA TEAM
2. Plan2.1 Roles and Responsibilities
FDA Team
PM (DungVH)
Design sub-team
(TanND)
QA(LinhCD)
Implemetation sub-team(DungVH)
Risk management
(TungLM)
Configuration management
(LinhVH)
Testing sub-team
(NgocUT)
Research sub-team(TungLM)
Document maintainer(LinhVH)
Document writer
(NgocUT)
TungLM TanND
DungVH TanND LinhCD TungLM
TungLM LinhVH LinhVH NgocUT
TungLM DungVH NgocUT LinhCD
NgocUT LinhVH
FDA TEAM
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
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 TeamFDA 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
FDA TeamFDA TEAM
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
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
4. Implementation
Draw region found not
filter in HSV image
FDA Team
Draw face detected
after filter in HSV
image
FDA TEAM
4. Implementation
Binary Matrix
FDA Team
Histogram of image color
All region’s information
Face detected in original image
FDA TEAM
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
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 TeamFDA 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.
FDA TeamFDA TEAM
5. Conclusion
Develop in Future
Develop in Future
Maintainability:Smart software like Neural network
Performance:Cloud computing
Availability:Code in C, C++
Reliability:Collect eyes sample
FDA TEAM