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Neural Network System for Face Detection

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fatimah albraidy

on 1 January 2014

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Transcript of Neural Network System for Face Detection

Neural Network System for Face Detection
Introduction
MATLAB
MATLAB (Matrix Laboratory)
is a high-level programming language solving high-performance problems with complex numeric calculations and visualization.
Neural Network
Proposed system

First Stage :
searching for potential face regions:
1. Skin color segmentation in YCbCr color space.
2. Label all four-connected components and find the center of each block.
3. Find any three centers of three different blocks forming an isosceles triangle.
4. Clip the potential face regions.

second Stage:
face verification
.
Feed the result into neural network system, and obtain the output (face or non-face).


Senior project -2013


Prepared by:
Sarah AlEssa 210043560
Asrar AlMoqbil 210023024
Areej AlQhtani 210038067
Huda AlSulmi 210039589
Fatimah AlBraidy 210042627

Advisor:
Dr. Gamal M. Behery
Outline
Introduction
Image processing
MATLAB
Neural network
Proposed system
Face detection system is automated system that takes an image and extract the face out of it.

face detection is an important prior step for face recognition system,

which is widely used as in security systems, face verification systems, telecommunication, video surveillance.

Objective
Aim of this project

is to construct an algorithm that demonstrates all the stages we need to go through in order to build an efficient face detection system.


In order to proceed in face detection algorithm, we need to cover 3 base knowledge areas:

Image processing
Neural network
MATLAB
Image Processing
Image
Digital Image
Binary image
Gray level image
Color image
Image Acquisition
Image Enhancement
Histogram Processing

Image segmentation is a process of partitioning the image into regions or segments that makes it easier to read its details or analyzing them.
Color Image processing

Skin Color Segmentation
In YCbCr color space, the color is
displayed as three numerical values that are put together to produce the resulted color, these components are:

Y luminance component( brightness)
Cb is blue minus luma (B - Y)
Cr is red minus luma (R – Y)
YCbCr color space
MATLAB and digital image processing
Image Processing Toolbox.
MATLAB and Neural Networks
Neural Network Toolbox.
Feed Forward Neural Network

Back-Propagation Neural Network
A Model of a Single Neuron
Feed-Forward neural network
Hidden Nodes and Layers
Training
Back Propagation Architecture
Back-Propagation Algorithm
This algorithm describes the back propagation in neural network with one hidden layer.

The algorithm includes three steps:
Feed foreword
Computing the error
Update the weights

First Stage :
1. Skin color segmentation in YCbCr color space:
2. Connected component labeling:
3. Forming an isosceles triangle:
4. Clip the potential face regions:
Second Stage :
Build the neural network system
Conclusion
After completing the stages:
1. searching for potential face regions
2. face verification
the face in the image should be detected, and the face region should be marcked using square shape around it -for example.
Questions?
Thank you for your attention.
Done by:
Fatimah AlBraidy Sarah AlEssa Asrar AlMoqbil Areej AlQhtani Huda AlSulmi
Full transcript