A robust automated attendance system using face recognition techniques PhD proposal; May 2009
Face Recognition Proposal Presentation
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Transcript of Face Recognition Proposal Presentation
Outline
Biometrics
Why we choose face recognition over other biometric
Applications of face recognition
Face recognition
Working process
Our current working progress
Advantages & limitations
Future work
Why we choose face recognition
over other biometric
It requires no physical interaction on behalf of the user.
It is accurate and allows for high enrolment and verification rates.
It does not require an expert to interpret the comparison result.
It can use our existing hardware infrastructure, existing cameras and
image capture Devices will work with no problems
It is the only biometric that allow us to perform passive identificationin a one to many environments (e.g.: identifying a terrorist in a busy
Airport terminal)
Advantages & Limitation
Advantages:
Its convenience and Social acceptability. all we need is our picture taken for it to
work.
Face recognition is easy to use and in many cases it can be performed without a
Person even knowing.
Face recognition is also one of the most inexpensive biometric in the market and Its
price should continue to go down.
Limitation:
Face recognition systems can’t tell the difference between identical twins.
Required Elements
Using software
MATLAB 8.1 (R2013a)
Hardware
USB PC Camera-168
Required products
Image Acquisition Toolbox
Image Processing Toolbox
Computer Vision System Toolbox
Face Detection
Algorithm used:
viola-jones algorithm (CascadeObjectDetector)
Working parameters:
detect FrontalFaceCART, LeftEye, RightEye, Mouth, and Nose simultaneously
Toolbox
Image Processing Toolbox
Computer Vision System Toolbox
Main functions:
detectFaceParts: Detects frontal faces with parts.
detectRotFaceParts: Detects faces with parts rotating an input image
Advantage:
The performance is improved compared to the default usage of the face detection
Face Parts Detection
Fig: Detected face part from group picture
Fig: Separated each of face part from this picture
Our Future work
After detection part we will match the detected image with a test
image.
We are also working to create a database for our input images.
Then we do the Recognition Part