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:

LOGO

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

FDA TEAM

Contents

Introduction1

Plan2

Requirements33

Implementation44

Conclusions5

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

Objective System

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

2.2 Software Process Model Iterative Approach to Development

FDA TeamFDA 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.

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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

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4. Implementation

4.1 System Architectural Design

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4. Implementation

4.2 Component Diagram

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4. Implementation

1

Skin pixel classification

2

Connectivity analysis

3

Skin region identified is a face or not

4.3 Face Detection Algorithm

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4. Implementation

Algorithm model process

FDA TeamFDA 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

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4. Implementation

Binary Matrix

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Histogram of image color

All region’s information

Face detected in original image

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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

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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

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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

Demo and Test

Demo FDA

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Q&A

Question & Answer

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LOGO

FDA Team