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Human Mouse Interface
By:ESHPREET BAJWA(Roll no. H-15)
SMITA CHANDURKAR(Roll no. H-27)ANAGHA GOLHAR(Roll no. J-19 )
Under guidance of:Prof. S.A. Khoje
A Project presentation on
INDEX Problem Definition Project Objectives Face detection methods SSR filter Skin color model SVM Face detection algorithm Face tracking H/W & S/W requirements Feasibility Future prospects
Problem definition
This project aims to present an application that is able of replacing the traditional mouse with the human face as a new way to interact with the computer.
Facial features (nose tip and eyes) are detected and tracked in real-time to use their actions as mouse events.
Project Objectives
Real Time Response. To enable physically handicapped people to use the
mouse. Nose tip = mouse pointer. Eye blink = mouse click.
Face detection methods
Two main categories: Feature-based methods, and image-based methods.
Feature-based methods
Image-based methods
Face Detection Algorithm Overview
Face detection general steps.
Find Face Candidates
We will be using feature based face detection methods.
This is to reduce the area in which we are looking for the face, so we can decrease the execution time.
To find face candidates the SSR filter will be used
SSR Filter
SSR Filter stands for: Six Segmented Rectangular filters. The sum of pixels squaren in each sector is denoted as S
along with the sector number.
SSR Filter.
How do we use SSR?
Integral Images
Integral image at location x, y contains the sum of pixels which are above and to the left of the pixel x, y
….contd.
We will need only 3 arithmetic operations to calculate the sum of pixels which belong to it
Sector = D – B – C + A. So each SSR filter requires
6*3 operations to calculate it.
Ideal SSR filter location for a face candidate
Skin Color Model Use the pure r and g values which are the R and G values of the RGB color
model in the absence of brightness, and they are calculated with the following equations:
r = R / (R + G + B) (1) g = G / (R+ G + B) (2) We move the r and g values to the (a, b) color space with the following
equations: a = r + g / 2 (3) b = √3 / 2 g (4) The range of ‘a’ is from 0 to 1, while the range of ‘b’ is from 0 to √3 / 2. 0.49<a<0.59 0.24<b<0.29
SVMSVM stands for: Support Vector Machines, which are new types of maximum margin classifiers.
The center of each cluster that is big enough is set with the following equations:x = [∑ x(i)] / n y = [∑ y(i)] / n i is the pixel from the cluster, n is the cluster’s area.
Find Pupils’ Candidates
Find the pixels that belong to a dark area , the sector is binarized with a certain threshold.
The clusters of the binarized sector are found.
Extract BTE Templates
After finding pupils candidates for each of the clusters (face candidates) we can extract BTE templates in order to pass them to the support vector machine.
We train our support vector machine on templates of size 35 * 21 pixels.
35 * 21 pixels that represent the original template.
An extracted template that was rotated back to a horizontal pupils’ alignment.The larger version is only for observation. It will not be used in our face detection process.
Face Tracking
Detecting The Eyebrows Eyes Tracking Tracking The BTE Tracking the nose tip Blink Detection
Hardware Requirementso Pentium IV processoro Webcam
Software Requirementso Windows XPo JAVA/JAVAX
Feasibility Technical Feasibility
Economical Feasibility
Operational Feasibility
Schedule Feasibility .
Future Works
o Improving the tracking robustness against lighting conditions.
o Adding the double left click and the drag modeo Adding voice commandso Enable/disable controlling the mouse with the face
Thank you